Predictive forecasting and data growth trend in cloud services

ABSTRACT

Provided are systems and methods for operating a computing system in a data center to assist in the management of the resources of the data center. In various examples, the computing system can monitor use of the resources by tenants. Using data obtaining by monitoring the resources, the computing system can predict the expected use of the resources. The predicted use, or growth data, can be used by various systems in the data center. The growth data can be used by a provisioning system to adjust the sizes of bundles of resources, so that the sizes of the bundles of resources more accurately reflect the manner in which tenants will use the bundles of resources when the bundles of resources are allocated to the tenants. The growth data can be used by a life cycle management system to proactively recycle data before a tenant runs out of storage space.

BACKGROUND

The present disclosure relates to predicting the future resource needsin a data center, and use of the prediction by systems in the datacenter to more efficiently manage the resources of the data center.

A data center can include various computing resources, includingprocessing resources, storage resources, and networking resources, amongothers. In some examples, the data center operator can lease computingresources to subscribers. For example, an organization can leasecomputing resources to operate a website. In these examples, the datacenter can organize computing resources into storage allocations, whichcan provide a way to compartmentalize and keep separate the resourcesbeing used by different tenants. The data center can further includeautomated systems for on-boarding new subscriptions and managing thedata in active storage allocations.

BRIEF SUMMARY

In various implementations, provided are systems and methods foroperating a computing system in a data center, where operations executedby the computing system can assist in the management of the resources ofthe data center. In various examples, the computing system can beconfigured to monitor use of the resources by tenants. Using dataobtaining by monitoring the resources, the computing system can predictthe expected use of the resources. The predicted use, or growth data,can be used by various systems in the data center. For example, thegrowth data can be used by a provisioning system to adjust the sizes ofbundles of resources, so that the sizes of the bundles of resources moreaccurately reflect the manner in which tenants will use the bundles ofresources when the bundles of resources are allocated to the tenants. Asanother example, the growth data can be used by a life cycle managementsystem to proactively recycle data before a tenant runs out of storagespace.

In various implementations, methods, including computer-implementedmethods, computing systems, and computer-readable medium can includetechniques for using growth data. These techniques can includemonitoring changes to data in a storage allocation. The storageallocation can include a set of computing resources from computingresources of the data center. The storage allocation can be associatedwith a tenant of the data center, where the data center enables usersassociated with the tenant to use the set of computing resources duringa subscription period. The storage allocation can be associated with acategory from a plurality of categories for storage allocations. Thetechniques can further include determining an expected resource usagefor the storage allocation. The expected resource usage can project anamount of computing resources the storage allocation will use after aperiod of time following a current time. The expected resource usage canbe determined using the changes to the data. The techniques furtherinclude determining that the expected resource usage is greater than asize of a bundle of resources from a resource pool. The bundle ofresources can include a set of unused computing resources that has beenpre-allocated for use as a new storage allocation of a same category asthe category for the storage allocation. The size of the bundle ofresources can correspond to an amount of the set of unused computingresources. The techniques further include instructing a provisioningsystem of the data center to increase the size of the bundle ofresources to correspond to the expected resource usage for the storageallocation. Increasing the size of the bundle of resources can includeallocating additional unused computing resources to the bundle ofresources.

In some aspects, before the provisioning system is configured toincrease the size of the bundle of resources, the bundle of resourceshas a first size. In these aspects, the storage allocation wasconfigured from a second bundle of resources from the resource pool, thesecond bundle of resources being the first size.

In some aspects, techniques implemented by the methods, computingsystems, and computer-readable medium discussed above can furtherinclude receiving a request for a second storage allocation. In theseaspects, the techniques can further include determining that the requestis associated with a subscription of the same category as the categoryfor the storage allocation. The techniques can further includeinstructing the provisioning system to configure the bundle of resourcesaccording to the request. When configured, the set of unused computingresources and the additional unused computing resources included in thebundle of resources can be assigned to the second storage allocation.

In some aspects, techniques implemented by the methods, computingsystems, and computer-readable medium discussed above can furtherinclude determining an expected number of storage allocations for thecategory. The expected number of storage allocations can project storageallocations for subscriptions expected to be received after the currenttime. These aspects can further include determining that the expectednumber of storage allocations is greater than a number of bundles ofresources associated with the category. These aspects can furtherinclude allocating additional bundles of resources. Allocating theadditional bundles of resources can include allocating additional unusedcomputing resources to each of the additional bundles of resources.

In some aspects, unused computing resources are computing resources thatare not allocated to storage allocations.

In some aspects, increasing the size of the bundle of resources includesallocating unused physical storage to the bundle of resources.

In some aspects, the data center includes a pool of physical resources.In these aspects, a first portion of the pool of physical resources canbe included in the storage allocation. A second portion of the pool ofphysical resources can be included in the bundle of resources. A thirdportion of the pool of physical resources is not allocated before theprovisioning system is instructed to increase the size of the bundle ofresources.

In some aspects, computing resources include processing resources,storage resources, or networking resources.

In some aspects, techniques implemented by the methods, computingsystems, and computer-readable medium discussed above can furtherinclude determining a mapping between a functional entity of the storageallocation and physical storage associated with the storage allocation.A functional entity can represent data in the storage allocation. Inthese aspects, the expected resource usage can be determined used themapping.

The foregoing, together with other features and implementations willbecome more apparent upon referring to the following specification,claims, and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a data center that includes an analysissystem that can determine future resource needs for tenants of the datacenter.

FIG. 2 illustrates an example of the operation of an analysis system ina data center.

FIG. 3 includes a chart illustrating an example of linear modelingapplied to data captured for an identity management service.

FIG. 4 illustrates another example of application of linear modeling topredict future growth.

FIGS. 5A and 5B illustrate examples of operations of a provisioningsystem in a data center, and use by the provisioning system of growthdata computed by an analysis system.

FIG. 6 illustrates an example of an interface that displays growth data.

FIG. 7 illustrates an example of a data center that includes life cyclemanagement for the storage allocations in the data center.

FIG. 8 illustrates an example of a process for using growth data toadjust bundles of resources in a resource pool.

FIG. 9 depicts a simplified diagram of a distributed system forimplementing examples discussed herein.

FIG. 10 is a simplified block diagram of a cloud-based systemenvironment.

FIG. 11 illustrates a computer system.

DETAILED DESCRIPTION

Cloud services include various hardware and software resources that arehosted by a provider of these resources for use by others (referred toas “customers,” “subscribers,” or “tenants”). For example, a cloudservices provider can operate a data center, which can include computingsystems executing various applications and network infrastructure. Inthis example, the cloud services provider can lease hardware,applications, and/or network infrastructure to tenants. The tenants canthen use the hardware, applications, and/or network infrastructureaccording to each tenant's particular needs. As another example, a cloudservices provider can itself be a tenant of another cloud servicesprovider. For example, a first cloud services provider can host aservice using the infrastructure (e.g., in a data center) of secondcloud services provider. The first cloud services provider can thenlease services to tenants.

Examples of the services models used by cloud service providers (alsoreferred to herein as “cloud providers” or “providers”) includeinfrastructure as a service (IaaS), platform as a service (PaaS),software as a service (SaaS), and network as a service (NaaS), amongothers. IaaS providers provide tenants with infrastructure resourcessuch as processing capacity, storage, networks, and other computingresources that the tenant is able to use to run software. The tenantdoes not manage the infrastructure, but has control over operatingsystems, storage, and deployed applications, among other things, and maybe able to control some networking components, such as firewalls. PaaSproviders provide a tenant with a platform on which the customer candevelop, run, and manage an application without needing to maintain theunderlying computing infrastructure. SaaS is a software licensing anddelivery model in which software is licensed to a tenant on asubscription basis, and is centrally hosted by the cloud provider. Underthis model, applications can be accessed, for example, using a webbrowser. NaaS providers provide network services to tenants, for exampleby provisioning a virtual network on the network infrastructure operatedby another party. In each of these service models, the cloud serviceprovider maintains and manages the hardware and/or software that providethe services, and little, if any, software executes on a user's device.

In various examples, a cloud services provider allocates to a tenant aset of resources, for which the tenant subscribes for a pre-determinedperiod of time. From the point of view of the tenant, the resources canbe organized into functional entities, such as user accounts, useraccount types, user account groups, and user account privileges, amongother things. An identity cloud Service, for example, can havefunctional entities such as user identity data, audit snapshots,authorization tokens, and process job histories, among other things. Aload balancer cloud service, as another example, can have functionalentities such as service logs, and routing requests, Domain Name Service(DNS) activity data, among other things Within an allocation for atenant, functional entities can map to logical entities, which canreflect the manner in which the cloud services provider manages thefunctional entities. For example, logical entities can includedatabases, database tables, and/or database entries in which informationfor the functional entities is organized and stored. The logicalentities can have corresponding physical entities, or physical hardwarewhere the data is stored. Examples of physical entities include datafiles and storage on a disk, among other things.

When a tenant subscribes to a cloud service, the tenant can receive anallocation of functional entities. For example, the tenant can receivean allocation of 100 user accounts each having an email account andallotment of 1 gigabyte (GB) of disk space. In this example, the tenantcan individually activate and put each user account into use. Once auser account is activated, the user account can generate and receiveemail, which is stored in the user account's disk space allotment.

In the preceding example, the tenant may not be able to determine therate at which the tenant's allocation of user accounts and disk space isbeing consumed. For example, the tenant may not have sufficient data tounderstand whether the tenant's allocation will run out in the next yearor the next week. Additionally, without an understanding of the rate atwhich resources are, or will be consumed, when the tenant's lease periodends and the tenant needs to renew, the tenant may not know how muchmore of an allocation the tenant should request. The cloud servicesprovider may be able to report to the tenant information such as currentphysical storage available or database table usage, but this informationmay not be sufficient for a tenant to project future needs.

A data center operator may also need to understand the rate at which theresources of the data center are being consumed. In some examples, thedata center operator pre-allocates resources in pre-set configurations,so that assigning allocations to tenants can occur very quickly. Thesizes of the pre-allocated resources can estimated; however, if theestimates do not reflect tenants' projected usage of the resources, thenthe pre-allocations may be inadequate and tenants may run out ofresources to quickly. As another example, without a projection of futureusage of the data center's resources, the data center operator may notbe able to determine when it may be necessary to add more resource andhow many resources to add.

In various implementations, provided are systems and techniques foranalyzing a tenant's usage of the tenant's storage allocation, andprojecting the tenant's growth, in terms of resource usage. In variousimplementations, an analysis system can collect information about thefunctional resources and the physical resources associated with atenant, and can forecast the data growth of the tenant in terms of thetenant's functional resources rather than only in terms of physicalresources. The data predicted for each tenant can include, for example,the average rate of storage consumption, a storage forecast for a givenperiod of time (e.g., the next seven days), the fastest growing resourceassociated with the tenant, resource data distribution, resource growthtrend, and data inflow and outflow for the tenant, among other things.Using this data, the analysis system can provide a comprehensiveanalysis of the tenant's future resource needs, which a data centeroperator can use to more efficiently manage the resources of the datacenter.

In addition to providing growth data for a tenant, in variousimplementations, an analysis system can further project growth fortenants of various types, for a data center as a whole, and/or acrossdata centers. A data center operator can use this growth data in variousways. For example, the growth data can be used by an automatedprovisioning system to adjust the size of pre-allocated bundles ofresources, so that the bundles of resources better anticipate the needsof various tenants. As another example, the growth data can be usedautomatically purge data and recover physical storage for a tenant, anoperation referred to herein as life cycle management. In this example,purging of a tenant's data can occur in a manner that is consistent withthe tenant's usage, rather than on a fixed schedule.

In addition to a provisioning system that anticipates tenant's resourceneeds and intelligent life cycle management, the growth data can haveadditional uses. For example, a tenant may have deployed software on thetenant's data center resources. In this example, should the softwarehave errors and behave incorrectly, the incorrect behavior may beevident in the growth data. For example, the growth data may show asudden spike in the usage of a resource. Software or users that arebehaving maliciously may also be detectable through the growth data.

FIG. 1 illustrates an example of a data center 104 that includes ananalysis system 120 that can determine future resource needs for tenantsof the data center 104. In various examples, a data center is a pool ofcomputing resources, including processing resources, storage resources,networking resources, software resources, and so on. To provide theresources, the example data center 104 can include computing serversthat have one or more central processing units (CPUs) for providingprocessing capacity, hard drives of various types for providing storagecapacity, and network connections that connect the servers to each otherand to networks outside the data center 104. The computer servers canfurther be executing software programs that enable tenants to use thehardware resources of the data center 104, and/or software programs thancan be offered as resources. For example, the servers can be executingoperating systems, hypervisors, virtual machines, web hosting platforms,software development platforms, networking platforms, and so on. Invarious examples, the operators of the data center 104 can be operatingmultiple data centers. In these examples, the data center operator mayuse the resources of multiple data centers as a common pool ofresources, and can allocate resources from different data centers to thesame tenant.

In some examples, the data center operator may be a cloud servicesprovider, and may be leasing hardware and/or software resources tocustomers or tenants. In some examples, a customer or tenant can itselfbe a cloud services provider. For example, a first tenant can obtainresources from the data center 104, and then provide services, such asweb hosting or software development platforms, to others, who becometenants of the first tenant. In some examples, the operator of the datacenter 104 may use the resources of the data center 104 partially orentirely for the operator's own uses. In the example of FIG. 1, theresources of the data center 104 are made available to others.

Tenants of the data center 104 can include organizations and/orindividual users. An organization is a collection of individuals workingtogether for a common purpose. Examples of organizations includecompanies, educational institutions, and governments, among others. Theindividuals of an organization can be represented in a computingenvironment as users, where a user is a digital entity represented by auser identifier, user account, authentication credentials, and/or accessprivileges, among other things. Herein, the terms user, user identifier,and user account may be used interchangeably. An organization can berepresented in a computing environment by the users of the organizationand/or the computer resources that are under the control of theorganization. For example, an organization may operate and control anetwork and/or may own a public network domain, such as “Oracle.com.”

A tenant that is an individual user is a digital entity that is notaffiliated with any specific organization. For example, an individualcan become a tenant of the data center 104 in order to operate awebsite.

Operation of the data center 104 of FIG. 1 differs from operation ofwhat is commonly referred to as an enterprise network in several keyaspects. An enterprise network, which may also be referred to as acorporate network, is the network infrastructure maintained andcontrolled by an organization and that connects computer devices andrelated devices of the organization together. Though the term“enterprise” is synonymous with the term “company,” the network of, forexample, a university of a government agency can also be considered anenterprise network. An enterprise network is often only accessible tothe users of the organization that controls the enterprise network.Maintenance and administration of an enterprise network is most oftenhandled by or under the direction of users of the organization.

In contrast, a data center such as the example data center 104 of FIG. 1is operated by one organization for the use of other organizations orusers. The organization that is the operator of the data center 104controls and maintains the data center 104. In various examples, theorganizations that are tenants of the data center 104 do not have directaccess to the hardware of the data center 104, and need not be concernedwith the maintenance of the data center 104. For security reasons,tenants of the data center 104 are also not aware of one another, andare not given overlapping resources. The data center 104 can thus enableorganizations and individual users to obtain computing resources morequickly and efficiently than when the organizations and users operate anetwork themselves. Enabling multiple, unrelated tenants to use theresources of the same data center 104, can lead to technical issues forthe data center operator, some of which are discussed further below.

In various examples, each tenant of the data center 104 receives astorage allocation, where the storage allocation includes the resourcesassigned to the tenant. For example, in the example illustrated in FIG.1, a tenant, Tenant-0 102, has been allocated one storage allocation110. Tenant-0 102 can be using the storage allocation 110 to run variousservices. For example, Tenant-0 102 can be using the storage allocation110 to run a Load Balancing as a Service (LBaas) service, a TenantAdministration Service, and/or an Identity Cloud Service, or anotherservice. These example services are services that can be offered toother tenants of the data center 104.

In the illustrated example, the resources in the storage allocation 110are organized into functional entities 112, logical entities 114, andphysical storage 116, each of which represent a different view of thesame resources. For example, the functional entities 112 represent anabstract view of the resources, while the physical storage 116 representa more literal view of the resources.

The functional entities 112 provide Tenant-0 102 with a functional viewof the resources allocated to Tenant-0 102, where the functional viewcan be defined according to the service for which Tenant-0 102 is usingthe storage allocation 110. Functional entities can include varioustypes of data structures, digital collections of data, or otherstructures that provide an abstract representation of data arranged inthe logical entities 114 and stored in the physical storage 116. Forexample, if Tenant-0 102 is using the storage allocation 110 to operatea banking service, the functional entities 112 can include useridentifiers, accounts of various types, and financial instruments suchas loans, lines of credit, and certificates of deposit, among otherthings. As another example, if Tenant-0 102 is using the storageallocation 110 to operate an online store, the functional entities 112can include products, departments, customer accounts, supplier accounts,email accounts, and a fulfillment system, among other things. In variousexamples, a data model can define the new functional entities 112, wherethe data model can be associated with a service for which Tenant-0 102is using the storage allocation 110.

In some examples, the functional entities 112 can be defined by Tenant-0102, and in some examples the service for which Tenant-0 102 is usingthe storage allocation 110 can define the functional entities 112. Forexample, Tenant-0 102 may be have configured the storage allocation 110as a compute farm, on which the tenant's users can executeprocessing-intensive computations. In this example, the functionalentities 112 associated with the compute farm service can includevirtual machines, virtual processors, and virtual network connections,among other things. As a further example, Tenant-0 102 may also havesubscribed to a load balancing service, which can distribute incomingcompute requests among the available virtual machines, virtualprocessors, and virtual network connections. The load balancing serviceof this example can be provided by the data center 104 as a separateservice, or can be provide by another service provider. In this example,functional entities 112 can be defined by the load balancing service toinclude service logs, routing requests, one or more routing hosts, DomanName Service (DNS) activity, and other load balancing activity orrecords. The service logs can provide Tenant-0 102 with a history ofrouting requests, where the routing requests list processing jobs, whenthe processing jobs were initiated, where the processing job wasexecuting, and when the processing job completed, among other things. Arouting host can represent a virtual routing agent that manages thesending of processing jobs to virtual machines. DNS activity can recorddomain name service requests by network devices connecting to thevirtual compute farm.

Another example of a service that can define functional entities 112 forthe storage allocation 110 is a tenant administration service, anIaaS-type service that can orchestrate between an organization'sworkflows and the available resources in the storage allocation 110. Forexample, the tenant administration service can generate a serviceinstance for a workflow, and can link the instance to resources such ascomputing resources, credentials, storage volumes, and networks, amongother things. In this example, the tenant administration service candefine functional entities such as service instances and user accountsthat enable access to the resources of the storage allocation 110.

In various examples, the functional entities 112 map to logical entities114. The logical entities 114 include various data structures that canbe used to store and organize the data associated with each of thefunctional entities 112. An example of a logical entity is a database.In various examples, the logical entities 114 can include one or moredatabases that store the data for the functional entities 112. Forexample, one database can store user identifiers and user credentials,and another database can store user activity logs. In various examples,one database can store information of different types. In variousexamples, the same data can be stored in different databases, where thedifferent databases are organized to access the data in different ways.

In various examples, a database can include one or more logical storageunits that can be referred to as table spaces, where the table spacesstore all of the database's data. In some examples, table spaces can begrouped into units that can be referred to as shards. A shard can be acategory of the database. In some examples, a shard has physical storagespace that is separate from other shards.

The table spaces can be divided into logical units of storage that canreferred to as segments. A segment can include one or more logical unitsreferred to as extents. An extent is a logical unit of database storagespace allocation, and have a specific data structure. In some examples,all of the extents of a segment are stored in the same table space. Asan example, a table's data can be stored in one segment, while thetable's indexes can be stored in a different segment. When a table orindex is partitioned, each partition can be stored in a separatesegment. In some examples, additional extents can be allocated to asegment when all of the space in the extents of a segment are full. Inthese examples, because extents are allocated as needed, the extents ofone segment may or may not be contiguous on a physical disk.

In various examples, extents are a collection of contiguous data blocks.A data block corresponds to a specific number of bytes of physical spaceon a disk. For example, a data block can be 8 kilobytes (KB), 16 KB, orsome other amount of physical storage. A data block can be stored ondisk in a data file, where the format of the file can depend on theoperating system or file system that manages the disk. In some examples,when a new extent is allocated to a segment, corresponding data blocksmay not be immediately allocated. Instead, data blocks may be allocatedas the space in the segment fills. In various examples, a database canbe enlarged by either: adding a new table space; adding a data file to atable space; or increasing the size of an existing data file.

In some examples, a database object, such as a table, an index, or alarge object, may be stored in one or more table spaces, and may spanmultiple data files. In some examples, database administration programsspecify operations on the database in terms of database objects ratherthan using the names of data files.

In the preceding examples, table spaces, segments, extents, and datablocks are each examples of different types of logical entities. Datablocks can also be referred to as logical blocks or pages. The precedingexamples provide one example organization for a database, and otherorganizations are possible.

In various examples, the logical entities 114 map to physical storage116. The physical storage 116 includes hard drives and othernon-volatile memory devices. For example, as noted above, a database caninclude one or more table spaces, and a table space can be divided intosegments. A segment can be divided into extents, where an extent is acollection of contiguous data blocks. The data blocks can be stored indata files on a physical disk. The size of a database, in terms ofstorage space on disk, can thus be a sum of the data files that make upall of the table spaces.

In various examples, Tenant-0 102 can be allocated more than one storageallocation in the data center 104. In these examples, Tenant-0 102 canuse more than one storage allocation for one service, and/or can useeach storage allocation for a different service. In some examples,Tenant-0 102 can be allocated storage allocations in different datacenters.

In various examples, use of the storage allocation 110 by Tenant-0 102is based on a subscription. For example, an organization can pay thedata center operator for use of the storage allocation 110, and inexchange can use the storage allocation 110 for a prescribed period oftime. In this example, at the end of the prescribed period of time,Tenant-0 102 can cease use of the storage allocation 110, or can renewthe subscription. In various examples, a contract can define the fee,the subscription period, the use to which Tenant-0 102 can put thestorage allocation 110, and/or the services to be provided by the datacenter 104 in exchange for the fee, among other things. In some example,the contract can define minimum service levels the data center 104 mustguarantee to Tenant-0 102, such as available physical resources (e.g.,storage or processing capacity) or network bandwidth, among otherthings.

In various examples, Tenant-0 102 can view and manage the storageallocation 110 through a user interface 106 provided by the data center104. In some examples, the user interface 106 provides an interface forall the storage allocations in the data center 104 that are assigned toTenant-0 102. When Tenant-0 102 has storage allocations in multiple datacenters, in some examples, the user interface 106 can provide one viewof all the storage allocations, including storage allocations indifferent data centers. Tenant-0 102 would not, however, view thestorage allocations of other tenants. In various examples,authentication credentials can be required for Tenant-0 102 and/or usersof Tenant-0 102 to access the user interface 106.

In various examples, the user interface 106 can enable Tenant-0 102 toconfigure the storage allocation 110. Configuring the storage allocation110 can include, for example, defining the functional entities 112 andtypes of data associated with each functional entity. Thereafter,Tenant-0 102 can use the user interface 106 to conduct administrativeoperations for the storage allocation 110, such as generating newfunctional entities 112 and assigning functional entities 112 to users.In some examples, the users of Tenant-0 102 can access the resources ofthe storage allocation 110 through the user interface 106. In someexamples, the users can access the resources over a network, withoutneeding to use the user interface 106. For example, the data center 104can provide an Application Programming Interface (API) or anotherinterface that can enable network-capable computing devices to connectto the data center 104.

In various examples, the data center 104 can include a separatedeveloper user interface 108, which the data center operator can use tomanage the data center 104. The developer user interface 108 can, forexample, provide operations personnel, who may be referred to asdevelopment and operations or “devops,” with the status of the computerservers in the data center 104, the load on each server, the currentstorage capacity of each server, and possibly also the processes runningon each server. As a further example, the developer user interface 108may be able to provide a listing of current tenants, a service categoryfor each tenant, resources currently assigned to each tenant, andcurrent utilization of those resources among other things.

The example data center 104 of FIG. 1 further includes automatedmanagement features, which can assist in the management of the resourcesof the data center 104. The automated management features include aprovisioning system 130, life cycle management 150, and an analysissystem 120.

In various examples, the provisioning system 130 can manage onboardingof new subscriptions and/or renewal of existing subscriptions.Onboarding includes handling requests for new subscriptions and settingup storage allocations from pre-allocated bundles of resources. Newsubscription requests can come from new tenants or from existingtenants. Renewing existing subscriptions may require only informing atenant that the subscription period for a storage allocation is ending,and starting a new subscription for the storage allocation. Renewing anexisting subscription can, alternatively or additionally, includereconfiguring an existing storage allocation and/or migrating anexisting storage allocation to a new storage allocation. In variousexamples, the operations of the provisioning system 130 are mostly orfully automated, and may require little to no assistance from humanadministrators. Onboarding and renewals is discussed further below.

In various examples, the life cycle management 150 can manage recyclingof physical storage in active storage allocations. Recycling of physicalstorage can include deleting certain data at certain times, which freesspace in the physical storage and makes the space available for newdata. In some examples, part of the service provided by the data center104 can include automated deletion of data that is older than a certainperiod. For example, some data may become duplicated over time, or maybecome stale or out of date. In some examples, the tenant may be able tospecify data that the tenant does not want kept past a certain date,which the life cycle management 150 can then delete when the date isreached. Alternatively or additionally, the tenant may be able tospecify data that should not be automatically deleted. Further examplesof the life cycle management 150 are discussed below.

In various examples, the analysis system 120 can collect informationabout active storage allocations, and can output a prediction of futureresource needs of each storage allocation, and/or of all the storageallocations, as a whole. The output of the analysis system 120 isreferred to herein as growth data. The process of determining growthdata can include identifying functional entities, determining themapping of functional entities to logical entities, determining themapping of logical entities to physical storage. The process can furtherinclude monitoring changes over time to the functional entities, logicalentities, and physical storage. The process can further includeperforming statistical analysis using the data produced by themonitoring to determine how many more functional entities, logicalentities, and/or how much more physical storage the storage allocationwill need in the next week, month, year, or some other future timeperiod.

In various examples, the growth data produced by the analysis system 120is consumed by various other systems in the data center 104. Forexample, the growth data can be provided to the user interface 106 forviewing by the tenant. In this example, the tenant can be provided witha projection of how many more functional entities the tenant may need inthe future, and/or how much more physical storage the tenant may need.As another example, the growth data can be provided to the developeruser interface 108 for viewing by the data center operator. In thisexample, the data center operator can use the data to plan futureexpansion of the resources of the data center 104. As another example,the growth data can be provided to the provisioning system 130, whichcan use the growth data to more efficiently allocate the data center'sresources when onboarding new subscriptions or renewing existingsubscriptions. As another example, the life cycle management 150 can usethe growth data to modify the rate at which data is recycled, and tootherwise make more intelligent decisions about reclaiming space in astorage allocation. The growth data can also be provided to othersystems 160, such as security analytics systems.

FIG. 2 illustrates an example of the operation of an analysis system 220in a data center 204. In this example, a first tenant, Tenant-0 202 a,is using the resources of a first storage allocation 210 a for one ormore services. For example, Tenant-0 202 a can be using the firststorage allocation 210 a to run an online banking service. The firststorage allocation 210 a includes functional entities 212 a, which canbe defined by Tenant-0 202 a, and/or data center 204. The functionalentities 212 a map to various logical entities 214 a in the firststorage allocation 210 a, which provide data structures for storing andorganizing data associated with the functional entities 212 a. Thelogical entities 214 a map to physical storage 216 a in the firststorage allocation 210 a, which can include data files on physicaldisks. In some examples, Tenant-0 202 a may have more than one storageallocation in the data center 204, being used for the same services ordifferent services. In various examples, Tenant-0 202 a can view andmanage the first storage allocation 210 a (and possibly also any otherstorage allocation) through a user interface 206 a provided by the datacenter 204.

The example of FIG. 2 further illustrates a second tenant, Tenant-1 202b. Tenant-1 202 b can be associated with the same organization asTenant-0 202 a. For example, Tenant-0 202 a can be one division of acompany, and Tenant-1 202 b can be a different division of the samecompany. Tenant-1 202 b can alternatively be part of an organizationthat is unrelated to Tenant-0 202 a. For example, Tenant-0 202 a can beassociated with a national bank, while Tenant-1 202 b be a business runby a sole proprietor.

In the example of FIG. 2, Tenant-1 202 b is using the resources of asecond storage allocation 210 b. The second storage allocation 210 bincludes functional entities 212 b, which can be similar to thefunctional entities 212 a of the first storage allocation 210 a, or canbe entirely different. For example, while Tenant-0 202 a can be usingthe first storage allocation 210 a to run an online banking service,Tenant-1 202 b can be using the second storage allocation 210 b to run awebsite that advertises the goods and services sold by the organizationwith which Tenant-1 202 b is associated. The functional entities 212 bof the second storage allocation 210 b also map to logical entities 214b, which further map to physical storage 216 b. Tenant-1 202 b can beprovided with a different instance of a user interface 206 b to view andmanage the second storage allocation 210 b. The instance of the userinterface 206 b provided to Tenant-1 202 b can be similar to the userinterface 206 a provided to Tenant-0 202 a, may include features orfunctionality available not to Tenant-0 202 a, and/or may not have allof the features or functionality available to Tenant-0 202 a.

In various implementations, the analysis system 220 can monitor thestorage allocations in the data center 204, and, using data produced bymonitoring the storage allocations, predict the future growth of thestorage allocations. In some examples, the analysis system 220 caninclude resource monitoring 222 and growth prediction 224 systems. Theresource monitoring 222 and the growth prediction 224 can be executed,for example, by software executing autonomously on a compute server inthe data center 204.

In various implementations, the analysis system 220 can operate instages. For example, in a first stage, the analysis system 220 candetermine the mapping of functional entities to logical entities, andthe mapping of logical entities to physical storage. As a furtherexample, in a second stage, the analysis system 220 can track changes tothe functional entities, logical entities, and physical storage. As afurther example, in a third stage, the analysis system 220 can conductvarious statistical analysis to predict the future needs, in terms offunctional entities, logical entities, and/or storage, each storageallocation may need, and/or that the data center 204 as a whole mayneed. In various examples, these example stages may be divided intofurther stages. In various examples, operations of the analysis system220 can include additional stages.

In various implementations, a first operational stage of the analysissystem 220 includes mapping the relationships between functionalentities, logical entities, and physical storage. These relationshipsmay not otherwise be available. For example, a data model may define thefunctional entities for a storage allocation, and that each functionalentity is provided with a certain amount of storage. For example, thedata model may specify that, for a functional entity such as a useraccount, the functional entity is associated with one gigabyte (GB) ofstorage space. The data model, however, may not describe a mapping ofthe functional entity to logical entities. For example, the data modelmay not indicate the table spaces, table indexes, and/or table recordsinto which the data for a user account is organized. Having thisrelationship may be necessary for understanding future physical storageneeds, because, for example, the manner in which a database grows maydictate the way in which physical storage is allocated to accommodatethe growth. For example, as provided by the examples discussed above, adatabase may grow by adding extents, and the size of the extents and/orthe need for adding extents can be dependent on the part of the databasethat needs more data. Thus having only the relationship between afunctional entity and the amount of storage allocated to the functionalentity may not provide enough information to predict the future storageneeds for the functional entity.

Management of the logical entities might be an automated operation,handled by the database software, and thus the organization of thelogical entities may not be information that is readily available. Themappings of functional entities to logical entities, and logicalentities to physical storage may, additionally, change over time as datais deleted and added. In some examples, the data model is a staticdescription, and may not reflect the utilization of new functionalentities or physical storage at any given time, or over a span of time.

The mapping data, however, when used to monitor the storage allocations,can provide a more comprehensive and accurate view of the tenant's useof resources. For this and various other reasons, the analysis system220 can map a storage allocation to discover the relationships betweenfunctional entities, logical entities, and physical storage. Forexample, the analysis system 220 can examine the structure of a logicalentity such as a database, and identify the functional entity orentities associated with each database entry. For example, a databaseentry may include a tag or a pointer to a functional entity, or to afunctional entity that is part of another functional entity (e.g., auser account that is one of multiple user accounts associated with thesame user identifier). In this example, the analysis system 220 cangenerate a data structure that stores any relationships betweenfunctional entities, and between functional entities and the logicalentities into which the functional entities are organized.

As a further example, the analysis system 220 can determine the datafiles in which each of the logical entities are stored. This informationcan be derived, for example, by examining the extents of a database, anddetermining the data files into which the data blocks of each extent arestored. A data block in an extent may identify a data file in which thedata block is stored.

In various examples, the analysis system 220 can construct a datastructure in which the analysis system 220 can store a mapping offunctional entities to logical entities, and logical entities tophysical storage. The data structure can be, for example, a multi-tieredassociative array or a similar data structure. In some examples, theanalysis system 220 maintains a separate mapping for each storageallocation.

In various implementations, in a second operational stage, the analysissystem 220 can monitor changes to the functional entities, logicalentities, and physical storage in the storage allocations. Monitoringcan be conducted by the resource monitoring 222 of the analysis system220. Changes can include increases or decreases in a number offunctional entities or logical entities being used, and/or increases ordecreases in an amount of physical storage being used. For example, theanalysis system 220 can monitor the number of functional entities 212 a,such as user accounts, allocated to the first storage allocation 210 a,and the number of functional entities 212 a actually being used at anygiven time. As another example, the analysis system 220 can maintain acount of table spaces, segments, extents, and/or data blocks included inthe logical entities 214 a at any point in time. As another example, theanalysis system 220 can determine the amount of physical storage 216 ain use in any given time versus the total physical storage 216 aallocated to the first storage allocation 210 a.

In various examples, the analysis system 220 can monitor changes to thefirst storage allocation 210 a and the second storage allocation 210 bover a period of time. For example, the analysis system 220 can monitorthe storage allocations for one week, one month, six months, a year, orsome other time period. During this period, the analysis system 220 canconduct a count of functional entities, logical entities, and/orphysical storage once an hour, every four hours, once a day, or at someother interval.

In various implementations, in a third operational stage, the analysissystem 220 can analyze data collected during monitoring of the storageallocations, and can output a prediction of the future growth of thestorage allocations. The predicted future growth is referred to hereinas growth data 226. The growth data 226 can be determined by the growthprediction 224 of the analysis system 220. In various examples, thegrowth data 226 can be output using well-known formats, which can enabledifferent systems in the data center 204 to easily consume the growthdata 226. For example, the growth data 226 can be output usingJavaScript Object Notation (json), eXtensible Markup Language (XML), oranother portable format.

In some examples, the growth data 226 includes expected growth for eachof the storage allocations. In these examples, the growth data caninclude future functional entity needs, future logical entity needs,and/or future physical storage needs. In some examples, the growth data226 for a storage allocation can separate report future needs fordifferent services for which a tenant is using a storage allocation. Forexample, if Tenant-0 202 a is using the first storage allocation 210 ato provide an online banking service, and Tenant-0 202 a is alsosubscribing to an identity management service, the growth data 226 canpredict the number of bank accounts the banking service may need in thenext year, and separately predict the number of user accounts theidentity management service may be managing in the next year.

In some examples, the growth data 226 predicts future resource needsacross storage allocations. For example, the growth data 226 can predictfuture resource needs for storage allocations of different types. Forexample, the first storage allocation 210 a and the second storageallocation 210 b can both be storage allocations of type “A,” in whichcase analysis system 220 can use monitoring data from both storageallocations to predict future resource needs for all storage allocationsof type “A.”

In some examples, the growth data 226 can predict future resource needsfor the data center 204 as a whole. For example, the growth data 226 caninclude future compute resources needs, future disk space needs, and/orfuture network capacity needs.

In some examples, the analysis system can use linear regression modelsto predict future growth. Linear regression attempts to model therelationship between two variables by fitting a linear equation toobserved data. One variable is considered the explanatory, orindependent variable, and the other is considered to be a dependentvariable.

In some examples, the analysis system can use a tool such as theprogramming language R to perform linear regression modeling. R providesa language and environment for statistical computing. For example, Rincludes a function lm( ) for fitting linear regressions, which uses aformula syntax to specify the form of the statistical model to be fit.That is, for a linear model regressing Y on X, can be expressed aslm(Y˜X), where the tilde is read as “is a modeled as a function of”Using this function, database storage needs, for example, can be modeledas a function of, for example, functional entities.

FIG. 3 includes a chart 300 illustrating an example of linear modelingapplied to data captured for an identity management service. In thisexample, the identity management service is providing identitymanagement for tenants in a data center. During a monitoring period, theidentity management service was servicing 225 tenants, and the analysissystem counted audit events for each tenant. Audit events, in thisexample, are a type of functional entity. The horizontal axis of thechart 300 of FIG. 3 represents audit event counts, in thousands. Theanalysis system 220 also recorded the size of the audit database, whichis a logical entity in this example. The vertical axis of the chart 300represents the audit database size, in megabytes. In the chart 300, datapoints that were collected are illustrated by dots.

In the chart 300 illustrated in FIG. 3, a dashed line 302 illustrates asimple linear regression model applied to the data. The dashed line 302represents a best-fit between the data points on the chart 300, and canbe expressed using the following equation:y=b ₀ +b ₁ x ₁

In the preceding equation, the term b₀ is the vertical intercept of thedashed line 302, and the term b₁ is the slope of the dashed line 302.The term x₁ is an independent variable; for example, in the chart 300,the term x₁ represents a data count for a functional entity. The term yis a dependent variable; for example, in the chart 300, the term yrepresents storage consumption for a functional entity. As an example,in the chart 300, the dashed line 302 intersects the vertical axis atb₀=112.9, and the slope of the dashed line 302 is b₁=0.001306. In thisexample, values for y can be projected for future values of x₁. That is,for future counts of audit events x₁, the storage needed for the auditcounts can be determined by computing y=112.9+(0.001306)(x₁).

FIG. 4 illustrates another example of application of linear modeling topredict future growth. FIG. 4 includes a chart 400 illustrating datacaptured for an identity management service. In this example, theanalysis system has counted user identities and storage used by eachuser identity. In the chart 400, the horizontal axis represents countsof user identities, and the vertical axis represents storage occupied byuser identities, in megabytes. In this example, user identities are atype of functional entity, and the storage occupied by the useridentities are a type of logical entity. As in the preceding example,the 225 tenants were monitored during the monitoring period. Data pointscaptured by the analysis system are illustrated by dots.

In this example, a dashed line 402 illustrates a best-fit between thedata points. The dashed line 402 can also be expressed by the equationdiscussed above. In this example, the dashed line 402 intercepts thevertical axis at b₀=−1.527, and the slope of the dashed line 402 isb₁=0.003396. In this example, for a future number of user identities x₁,the storage needed for this number of user identities can be determinedby computing y=−1.527+(0.003396)(x₁).

In various examples, data can be captured across multiple dimensions.For example, storage consumption, in terms of database tables, indexes,partitions, unstructured binary data, and so on, can be mapped tofunctional entities, functional entity states, time, and so on. In theseand other examples, forecasting can be conducted for different factors,in addition to or instead of entity growth. For example, forecasting canbe conducted using time and as the independent variable.

Multi-dimensional linear regression can be used to predict futureresource needs that are based on multiple factors. For example, thelinear regression function lm( ) of the language R can be used to model,for example storage needs as a function of multiple, independentvariables, such as a number of users, a number of applications, or acount of authorization tokens, among other things. That is, inlm(y˜x+users+applications+authorization tokens), y can represent storageneeds, x can represent counts of functional entities, with users,applications, and authorization tokens provided as examples offunctional entities.

Linear regression modeling is one example of a technique that can beused to forecast resource needs based on past resource usage. Otherstatistical analysis techniques can be used, such as, for example,population mean, population standard deviation, population variance,sample mean, sample standard deviation, sample variance, and pooledsample standard deviation, among others.

Various systems in a data center can make use of the growth dataproduced by an analysis system such as is described above. Aprovisioning system is one example of a system in a data center that canconsume the growth data.

FIGS. 5A and 5B illustrate examples of operations of a provisioningsystem 530 in a data center 504, and use by the provisioning system ofgrowth data computed by an analysis system 520. In the exampleillustrated in FIGS. 5A and 5B, the data center 504 receives newsubmission request 570 for a new storage allocation. The submissionrequest 570 is received from a tenant, Tenant-0 502, which can be anexisting tenant of the data center 504, or can be a new tenant.

In various examples, the data center 504 receives the submission request570 at a request handler 538. The request handler 538 can be anautomated system that communicates with Tenant-0 502 to determine thetenant's needs and to establish a service contract with Tenant-0 502.For example, the request handler 538 can present a set of questions toTenant-0 502, which, when answered by the tenant, can indicate theservice or services for which Tenant-0 502 intends to use a storageallocation. In various examples, the request handler 538 can use thisinformation to determine a category for the tenant's subscription. Insome examples, the provisioning system 530 determines the subscriptioncategory, and communicates this information to the request handler 538.The category can determine the parameters of the service contract,including, for example, the size of the storage allocation given toTenant-0 502, as discussed further below. The service contract canfurther specify parameters such as a term for the subscription (e.g.,six months, one year, three years, or some other time period), servicelevels the data center 504 will provide (e.g., minimum compute, storage,and networking services), life cycle management parameters, and otherterms. In various examples, communication with Tenant-0 502 occursthrough a series of screens presented using a user interface, such as aweb browser, and can occur without assistance from a human operator.

Once Tenant-0 502 has consented to the service contract, the requesthandler 538 can communicate to the provisioning system 530 that a newsubscription has been received. In various implementations, theprovisioning system 530 can include an onboarding system 532 thatmanages the configuration of new storage allocations and inventorymanagement 534 that manages a pre-allocated resource pool 540. In someexamples, the operations of the onboarding system 532 and the inventorymanagement 534 are conducted by one compute server executingadministrative tasks.

In various examples, the onboarding system 532 is an automated systemthat uses the category for a new subscription to configure a new storageallocation 510. The onboarding system 532 can be, for example, softwareexecuting on compute server that has been dedicated to administrativetasks. The onboarding system 532 can have access to a set of storagetemplates 536. The storage templates 536 can include one or moretemplates for each subscription category, where a template can provideinformation such as a size for the new storage allocation 510, adescription of functional entities 512 that can be defined for thestorage allocation 510, an organization of the logical entities 114 forthe storage allocation 510, the size of physical storage 516 for thestorage allocation 510, services for which the storage allocation 510can be used, and/or services that can be activated for the storageallocation 510 by the tenant. As an example, the storage templates 536can include one set of templates each for categories “small,” “medium,”and “large,” where these categories describe an approximate size of theresources to which a tenant is subscribing. As an example, the storagetemplates can describe an amount of physical storage and/or the size ofthe table spaces that a tenant of a particular category will receive. Inthe example illustrated in FIG. 5A, the new subscription from Tenant-0502 has been classified as a “medium” category subscription.

Once the onboarding system 532 has identified a storage template for thenew subscription, the onboarding system 532 communicates thisinformation to the inventory management 534. In various examples, theinventory management 534 is an automated system that manages a resourcepool 540 of the data center 504. The inventory management 534 can beconducted by, for example, software executing on a compute server in thedata center 504.

The resource pool 540 includes the hardware and software resources ofthe data center 504 that are not currently in use by tenants. In variousexamples, these resources are organized into pre-allocated bundles ofresources. Pre-allocated, in this context, means that a bundle ofresources includes a certain amount of physical storage that has beenassigned to the bundle of resources. This physical storage is notavailable to other bundles of resources, or to active storageallocations. In some cases, a bundle of resources can also includepre-configured logical entities, such as some basic database structuresloaded onto the physical storage. A bundle of resources can also includesome pre-configured functional entities, or may include no functionalentities.

In various examples, different bundles of resources can be pre-allocatedfor different categories of subscriptions. For example, in the exampleillustrated in FIG. 5A, the resource pool 540 includes bundles ofresources 542 a for the “small” category, bundles of resources 542 b forthe “medium” category, and bundles of resources 542 c for the “large”category. In this example, the bundles of resources 542 a for the“small” category include fewer resources than the bundles of resources542 b for the “medium” category, which in turn includes fewer resourcesthan the bundles of resources 542 c for the “large” category. Forexample, the bundles of resources 542 a for the “small” category caneach include 11 GB of physical storage, the bundles of resources 542 bfor the “medium” category can each include 31 GB of physical storage,and the bundles of resources 542 c for the “large” category can eachinclude 51 GB of physical storage. Within each category, the bundles ofresources can otherwise be the same or have some variations, such ashaving logical and/or physical entities preconfigured.

Pre-allocating bundles of resources enables the provisioning system 530to quickly convert a bundle of resources into an active storageallocation. For example, in the example illustrated in FIG. 5A, havingbeen provided with a storage template for the “medium” category, theinventory management 534 can select a bundle of resources from theresource pool 540 from the bundles of resources 542 b for the “medium”category. The inventory management 534 can indicate the selection to theonboarding system 532, which can configure the selected bundle ofresources into the new storage allocation 510. Because the selectedbundle of resources includes pre-allocated physical storage and possiblyalso some logical entities, the onboarding system 532 may only need toremove the bundle of resources from the resource pool 540, configure thedata center's administrative systems to recognize the bundle ofresources as a new storage allocation 510, and enable access to thestorage allocation 510 by Tenant-0 502. In some examples, the onboardingsystem 532 can also configure some functional entities 512 for thestorage allocation 510, and/or Tenant-0 502 can configure the functionalentities 512.

The process of converting a bundle of resources into a new storageallocation can possibly be accomplished in a few minutes or in less thanan hour or a few hours. Without the pre-allocated bundles of resources,activating a new storage allocation can require multiples of hours orpossibly even days. This is because unused physical storage would needto be identified and allocated, then logical and functional entitieswould need to be configured on the physical storage. Even with automatedsystems, these steps can be time consuming. Pre-allocating bundles ofresources can save the time needed to bring up a storage allocation, sothat the tenant can put the storage allocation into use with very littledelay after issuing the submission request 570.

The storage allocation 510 should provide Tenant-0 502 with sufficientresources for the subscription period. For example, if the storageallocation 510 includes 31 GB of physical storage 516 and thesubscription period is one year, then Tenant-0 502 should not use up all31 GB before the end of one year. The configuration of the bundles ofresources in the data center 504, including the physical storage sizefor bundles of resources of different categories, may only be estimatedby the data center operator. These estimates may be based on pastinformation, and not include projected resources needs. Additionally,these estimates may not reflect actual usage of the storage allocations,or usage that was not contemplated when the estimates were made. Forexample, Tenant-0 502 may use up all 31 GB of physical storage 516 insix months.

In various implementations, the provisioning system 530 can use growthdata output by an analysis system 520 to make adjustments to the storagetemplates and bundles of resources in the resource pool 540, to reflectfuture resource needs. As illustrated in FIG. 5B, once the new storageallocation 510 is active and being used by Tenant-0 502, an analysissystem 520 can monitor the storage allocation 510 for changes to thefunctional entities 512, logical entities 514, and physical storage 516.Using data obtained from monitoring the storage allocation 510, theprovisioning system 530 can predict the future resource needs of thestorage allocation 510. As discussed above, the provisioning system 530can obtain data from all the storage allocations in use in the datacenter 504 to determine growth data.

In various examples, the inventory management 534 can periodicallyreplenish the resource pool 540, so that a certain number of bundles ofresources are maintained. For example, the inventory in the bundles ofresources can be replenished once per week, once per month, or overanother time interval. In various examples, the number of bundles ofresources maintained in the resource pool 540 is based on certainassumptions, such as, for example, the number of new subscriptionsexpected to be received in the next week, month, year, or some othertime interval, and the categories that new subscriptions will fall into.

In various examples, adjustments the provisioning system 530 can makecan include changing the size of the bundles of resources. For example,the inventory management 534 can be instructed to reduce or increase thesize of the bundles of resources 542 b for the “medium” category.Reducing the size of a bundle of resource can include deallocatingphysical storage from the bundle of resources, and putting thedeallocated physical storage into a pool of available resources.Increasing the size of a bundle of resources can include adding physicalstorage from the pool of available resources to the bundle of resources.In some cases, the data center operator may need to add physicalresources in order for any unallocated physical storage to be available.In some cases, changing the size of a bundle of resources can alsoinclude modifying the configuration of logical and/or physical entitiesin the bundle of resources.

Changes to the bundles of resources can reflect projected resourceneeds. For example, the storage allocation 510 assigned to Tenant-0 502may have been from the bundles of resources 542 b for “medium” categorysubscriptions, and may include 31 GB of physical storage 516. In thisexample, the growth data from the provisioning system 530 can indicatethat Tenant-0 502 will need 50 GB by the end of the subscription period.Data from other storage allocations from the “medium” category can beaggregated, to determine that tenants using these storage allocations,on average, will be using 50 GB of storage by the end of a similarsubscription period. This indicates that 50 GB is a better size for thebundles of resources in the “medium” category than is 31 GB. Thus, inthis example, the inventory management 534 can increase the physicalstorage of the bundles of resources 542 b in the “medium” category to 50GB. In this example, future subscriptions for the “medium” category willbe given storage allocations having 50 GB of physical storage, insteadof 31 GB.

The growth data can indicate the future resource needs in severaldifferent ways. For example, the growth data can indicate the number offunctional entities Tenant-0 502 will be using by the end of thesubscription period. In this example, growth data can include the sizeof physical storage that corresponds to the number of functionalentities. As another example, the growth data can indicate a number oftable spaces Tenant-0 502 will be using, and an amount of physicalstorage that corresponds to the number of table spaces. In additionalexamples, future resource needs can be expressed in various combinationsof functional entities, logical entities, and/or physical storage space.

As such, the storage templates 536 can also be adjusted to reflectfuture resource needs. For example, the storage allocation 510 may havecome with fifty user accounts when first configured, and the growth datamay indicate that Tenant-0 502 will be using 100 user accounts by theend of the subscription period. The same may be true for other tenantsthat were given a “medium” category storage allocation. In this example,the provisioning system 530, for example using the onboarding system532, can adjust a storage template for “medium” category subscriptionsto increase the number of user accounts that come with the subscription.In some examples, a change to the number of functional entities in astorage template can also include a change to the number of logicalentities that correspond to the functional entities.

Adjustments the provisioning system 530 can make to the resource pool540 can, alternatively or additionally, include change the number ofbundles of resources for different categories. For example, the resourcepool 540 may initially include four bundles of resources 542 a in the“small” category, three bundles of resources 542 b in the “medium”category, and two bundles of resources 542 c in the “large” category.These quantities can reflect an expectation of the data center operatorthat most subscriptions will fall into the “small” category, that fewerwill fall into the “medium” category, and fewer still will fall into the“large” category. The growth data from the analysis system 520, however,may indicate that most subscriptions will fall into the “medium”category. For example, the growth data may indicate that tenants givenstorage allocations in the “small” category will use about as manyresources as do tenants using storage allocations in the “medium”category. In these example, the inventory management 534 can beinstructed to increase the number of bundles of resources 542 bmaintained in the resource pool 540 for the “medium” category, so thatthese bundles of resource can be available for future subscriptions.

In various examples, the provisioning system 530 can also managesubscription renewals. For example, when Tenant-0 502 subscription forthe storage allocation 510 is nearing the end of the subscription term,the data center 504 can send Tenant-0 502 an automated message askingTenant-0 502 whether the tenant wishes to renew the subscription. IfTenant-0 502 does not renew the subscription, then the resources in thestorage allocation 510 can be added back to the resource pool 540.

If Tenant-0 502 renews, then, in some implementations, the provisioningsystem 530 can determine whether the resources of the storage allocation510 are adequate to meet the tenant's needs, or need to be increased.For example, the provisioning system 530 can use the growth data fromthe analysis system 520 to determine the tenant's future resource needs.The growth data can indicate, for the next subscription period, that thetenant's use of the storage allocation 510 will not exceed the currentresources of the storage allocation 510. Alternatively, the growth datamay indicate that the tenant will need additional resources, as well ashow many additional resources the tenant will need. In this situation,the provisioning system 530 can provide the tenant with more resourcesthan are currently available in the storage allocation 510.

Increasing the resources for an existing tenant can be accomplished inseveral different ways. In one example, the physical storage 516 of anexisting storage allocation 510 can be increased. In this example, theadditional physical storage 516 can be occupied over time, as more datais generated by the functional entities and/or the size of the logicalentities increase. One advantage of increasing the physical storage 516is that the storage allocation 510 may not need to be taken offline todo so, or may need to be taken offline for only a short period of time.A disadvantage of only increasing the physical storage 516 is that useof the physical storage 516 may be less efficient than when the physicalstorage 516 is allocated as part of a bundle of resource.

Another method of increasing the resources for an existing tenant is topromote the tenant to a different category, one that is associated withlarger bundles of resource pool 540. In this case, the tenant can beassigned a new bundle of resources from the resource pool 540. The newbundle of resources may be larger due to the subscription having beenupgraded to a larger category. Alternatively, the bundle of resourcesmay be larger due to the size of the bundle of resources having beenincreased. In either case, the data in the existing storage allocation510 can be migrated to a new storage allocation configured from thebundle of resources. Migrating the data can require taking the storageallocation 510 offline, which results in an interruption of service tothe Tenant-0 502. Migrating the data to a new storage allocation can,however, result in the new storage allocation having a betterorganization, which can lead to more efficient use of the resources inthe new storage allocation.

In various examples, the growth data from the analysis system 520 canalso be provided to the tenants and/or to the data center operator. Forexample, the growth data for the storage allocation 510 can be providedto user interface 506, through which Tenant-0 502 can view the growthdata. In this case, the growth data provided to the user interfaceincludes only the growth data for the storage allocation 510, andpossibly also other storage allocations assigned to Tenant-0 502.

As a further example, the growth data can be provided to a developeruser interface 508, through which the data center operator can view thegrowth data. FIG. 6 illustrates an example of an interface 600 thatdisplays growth data. In various examples, the interface 600 can displaythe growth data for individual storage allocations, for individualtenants (who may have more than one storage allocation), and/or for thedata center as a whole. For example, the interface 600 can display theaverage rate of consumption of storage resources in the data center, anda forecast or prediction of storage that will be consumed in the nextseven days (or another time period). As a further example, the interface600 can display the fastest growing resource (e.g., the resource that isbeing consumed most quickly) in the data center. To assist the datacenter operator in determining future resource needs, the interface 600can display a distribution of data that resources are being used for andgrowth trends for the different types of data. The interface 600 canalso display data inflows and outflows; that is data being added orremoved from storage allocations. Using this information, the datacenter operator can determine whether or when to add computingresources, storage resources, and/or networking resources to the datacenter.

Another system that can make use of the growth data from the analysissystem is life cycle management. FIG. 7 illustrates an example of a datacenter 704 that includes life cycle management 750 for the storageallocations in the data center 704. In this example, a first tenant,Tenant-0 702 a, is using the resources of a first storage allocation 710a for one or more services. For example, Tenant-0 702 a can be using thefirst storage allocation 710 a to run an online banking service. Thefirst storage allocation 710 a includes functional entities 712 a, whichcan be defined by Tenant-0 702 a and/or data center 704. The functionalentities 712 a map to various logical entities 714 a in the firststorage allocation 710 a, which provide data structures for storing andorganizing data associated with the functional entities 712 a. Thelogical entities 714 a map to physical storage 716 a in the firststorage allocation 710 a, which can include data files on physicaldisks. In some examples, Tenant-0 702 a may have more than one storageallocation in the data center 704, being used for the same services ordifferent services. In various examples, Tenant-0 702 a can view andmanage the first storage allocation 710 a (and possibly also any otherstorage allocation) through a user interface 706 a provided by the datacenter 704.

In the example of FIG. 7, a second tenant, Tenant-1 702 b is using theresources of a second storage allocation 710 b. The second storageallocation 710 b includes functional entities 712 b, which can besimilar to the functional entities 712 a of the first storage allocation710 a, or can be entirely different. For example, while Tenant-0 702 acan be using the first storage allocation 710 a to run an online bankingservice, Tenant-1 702 b can be using the second first storage allocation710 b to run a website that advertises the goods and services sold bythe organization with which Tenant-1 702 b is associated. The functionalentities 712 b of the second storage allocation 710 b also map tological entities 714 b, which further map to physical storage 716 b.Tenant-1 702 b can be provided with a different instance of a userinterface 706 b to view and manage the second storage allocation 710 b.The instance of the user interface 706 b provided to Tenant-1 702 b canbe similar to the user interface 706 a provided to Tenant-0 702 a, mayinclude features or functionality not available to Tenant-0 702 a,and/or may not have all of the features or functionality available toTenant-0 702 a.

In various examples, the life cycle management 750 is an automatedsystem that can periodically clean up data in the storage allocations.Data clean-up may otherwise need to be conducted manually by the tenant.Cleaning up data can include deleting certain data, which frees up thespace occupied by the data and makes the space available for new data.Periodic clean-up of data can also be referred to as purging. Data canbe deleted, for example, by deleting a functional entity, which candelete database entries and data in physical storage that is associatedwith the functional entity. As another example, deleting data caninclude removing data from table entries (e.g., among logical entities),which can result in data in the physical storage being deleted. In thisexample, one or more functional entities associated with the tableentries is not removed, but may occupy less overall storage space. Asanother example, data files in the physical storage can be deleted. Inthis example, corresponding logical entities may change (e.g., adatabase may be able to remove an extent due to the data blocks for theextent having been deleted), though this need not be the case.Additionally, a functional entity that corresponds to the data files maybe, but need not be deleted.

In various examples, life cycle management occurs on a periodic basis.The period and/or the data that are cleaned out can be specified, forexample, in the service contract between the data center 704 and atenant. For example, the service contract can specify that data that isolder than 90 days (which can be referred to as the retention period)will be deleted. As a further example, the service contract may alsospecify that the data not have been modified in the last 90 days, orelse the data is considered still in use. In various examples, theservice contract and/or the tenant can specify the data retention timeperiod. In various examples, the service contract and/or the tenant canspecify the types of data that can be deleted once the retention periodexpires, and/or can specify types of data that will never be deleted.For example, the tenant may want to retain audit data for six months,but may only want to retain history data for 30 days.

In some examples, life cycle management can be implemented using aresource purge module. In these examples, the data center 704 caninclude a resource purge module for each tenant and/or for each storageallocation 710 a-710 b.

In various examples, the life cycle management 750 can use growth dataoutput by an analysis system 720 to more efficiently clean up data inthe storage allocations. The analysis system 720 can monitor the storageallocations, and predict the future growth of the functional entities,logical entities, and physical storage in the storage allocations. Invarious examples, the analysis system 720 can output the prediction asgrowth data, which can be input into the life cycle management 750system.

The growth data can enable the life cycle management 750 to free upspace in the storage allocations outside of the data retention policyspecified by the service contract, so that tenants can make moreefficient use of the space available to each tenant. For example, usingthe growth data from the analysis system 720, the life cycle management750 can determine that the physical storage 716 a in the first storageallocation 710 a will be used up in the next week, while the end of thecurrent retention period is still 60 days away. In this example, thelife cycle management 750 can take several actions to prevent Tenant-0702 a from running out of space in the physical storage 716 a. Forexample, the life cycle management 750 can determine to delete dataearly; that is, the life cycle management 750 can delete data that wouldotherwise be deleted when the retention period ends. In this example,the data that is deleted can be the oldest data.

As another example, the life cycle management 750 can inform Tenant-0702 a that the tenant will run out of space in the next week, and canask Tenant-0 702 a whether the tenant wants to delete some data. In someexamples, the life cycle management 750 can use the growth data tosuggest to Tenant-0 702 a data to delete. For example, the life cyclemanagement 750 can indicate, in the user interface 706 a, the oldestdata. Alternatively or additionally, the life cycle management 750 canindicate to Tenant-0 702 a the functional entities 712 a, logicalentities 714 a, and/or physical storage 716 a that is growing thefastest, and thus may need maintenance. As another example, the lifecycle management 750 can indicate to Tenant-0 702 a the data that isgrowing the slowest, which may be the oldest and thus least needed data.In these and other examples, the life cycle management 750 can displayprompts in the user interface 706 a, which Tenant-0 702 a can use toselect data to delete or retain.

In various examples, other systems in a data center can make use of thegrowth data than can be output by an analysis system. For example, faultcheckers and security systems can make use of the growth data. Thegrowth data can indicate that some data has increased in size inunexpected and/or sudden ways. For example, certain types of data may beexpected to grow at a steady pace, but the growth data instead showsthat the data will increase exponentially in size. As another example,the growth data may show that data of a certain type may have suddenlyspiked in size, when it was expected that the data should only grow at acertain rate.

In these and other examples, unexpected changes in the rate at whichdata is increasing can indicate several different problems in a storageallocation. For example, software being run in the storage allocationmay be defective or malicious, and may be generating data in anuncontrolled or undesirable manner. For example, the software may beperpetrating a denial of service attack by occupying resources in thestorage allocation to the point where the tenant is unable to use theresources. As another example, users associated with a tenant may beusing the resources of the storage allocation in an undesirable orunauthorized manner. For example, users may be uploading movies intouser accounts in the storage allocation. In these and other examples, afault checking system and/or a security system may detect unwanted datagrowth by examining the growth data.

FIG. 8 illustrates an example of a process 800 for using growth data toadjust bundles of resources in a resource pool, so that the bundles ofresources better reflect expected resource usage when the bundles ofresources are used as a storage allocation. In various implementations,the process 800 can be executed by a computing system executing in adata center.

At step 802, the process 800 includes monitoring changes to data in astorage allocation, wherein the storage allocation includes a set ofcomputing resources from computing resources of the data center, whereinthe storage allocation is associated with a tenant of the data center,wherein the data center enables users associated with the tenant to usethe set of computing resources during a subscription period, and whereinthe storage allocation is associated with a category from a plurality ofcategories for storage allocations. In various examples, computingresources can include processing resources, storage resources, and/ornetworking resources. In some examples, changes to the data in thestorage allocation can include increases and decreases in the size ofthe data. In some examples, changes to the data can include increasesand decreases in the number of functional entities, the number oflogical entities, and/or the amount of physical storage being used at amoment in time. In some examples, the changes are monitored over aperiod of time, such as a number of hours, a number of days, a number ofweeks, a number of months, or a different period of time.

At step 804, the process 800 includes determining an expected resourceusage for the storage allocation, wherein the expected resource usageprojects an amount of computing resources the storage allocation willuse after a period of time following a current time, and wherein theexpected resource usage is determined from the changes to the data. Insome examples, determining the expected resource usage can includeconducting a statistical analysis, such as a linear regression, usinginformation determined from the changes to the data. In some examples,the expected usage is projected for a period of time, such as a day,seven days, 30 days, or another time period, where the time period isafter a current time. At the current time, the expected usage can bedetermined and reported, for example to the tenant and/or to the datacenter operator. Alternatively or additionally, the expected usage canbe recorded for record keeping purposes and/or for later use.

At step 806, the process 800 includes determining that the expectedresource usage is greater than a size of a bundle of resources from aresource pool, wherein the bundle of resources includes a set of unusedcomputing resources that has been pre-allocated for use as a new storageallocation of a same category as the category for the storageallocation, wherein the size of the bundle of resources corresponds toan amount of the set of unused computing resources. Unused computingresources are computing resources that are not allocated to storageallocations. Being unused means that the computing resources are notbeing used by any tenant. The unused computing resources may be idle. Invarious examples, the bundle of resources in which the unused computingresources are included has been pre-allocated for future use as astorage allocation. Pre-allocating the bundle of resources enables a newstorage allocation to be configured quickly. For example, the bundle ofresources may only need to be configured for the particular service orservices for which the tenant intends to use the new storage allocation.

The bundle of resources includes a size, in terms of the amount ofcomputing resources included in the bundles of resources. For example,the bundle of resources can include a certain amount of physicalstorage. The size of the bundle of resources may have been estimated bythe data center operator. When the expected resource usage, determinedat step 804, is greater than the size of the bundle of resources, thesize of the bundle of resources may not accurately reflect the actualusage of the resources, once the resources are made available to atenant as a storage allocation.

At step 808, the process 800 includes instructing a provisioning systemof the data center to increase the size of the bundle of resources tocorrespond to the expected resource usage for the storage allocation,wherein increasing the size of the bundle of resources includesallocating additional unused computing resources to the bundle ofresources. In some examples, increasing the size of the bundle ofresources can include allocating additional computing resources to thebundle of resources. For example, increasing the size of the bundle ofresources can include allocating unused physical storage to the bundleof resources.

In some examples, before the provisioning system is configured toincrease the size of the bundle of resources, the bundle of resourceshas a first size. In these examples, the storage allocation could havebeen configured from a second bundle of resources from the resourcepool, the second bundle of resources being the first size. In someexamples, the resource pool can include bundles of resources ofdifferent categories. For example, the resource pool can include a“small” category, including bundles of resources having a certain size;a “medium” category that has bundles of resources that are somewhatlarger; and a “large” category that has bundles of resources that areeven larger. Size, in this context, refers to the amount of computingresources allocated to each bundle of resources. In these and otherexamples, the storage allocation could have been configured from abundle of resources from a certain category (e.g., the “medium”category). Additionally, at step 808, the provisioning system can beinstructed to increase the size of each of the bundles of resources inthis category (e.g., the “medium” category).

In some examples, the process 800 can further include receiving arequest for a second storage allocation. In these examples, the process800 can further include determining that the request is associated witha subscription of the same category as the category for the storageallocation. The process 800 can further include instructing theprovisioning system to configure the bundle of resources according tothe request, wherein, when configured, the set of unused computingresources and the additional unused computing resources included in thebundle of resources are assigned to the second storage allocation.

In some examples, the process 800 can further include determining anexpected number of storage allocations for the category, wherein theexpected number of storage allocations project storage allocations forsubscriptions expected to be received after the current time. In theseexamples, the process 800 can further include determining that theexpected number of storage allocations is greater than a number ofbundles of resources associated with the category. The process 800 canfurther include allocating additional bundles of resources, whereinallocating the additional bundles of resources includes allocatingadditional unused computing resources to each of the additional bundlesof resources.

In some examples, the data center includes a pool of physical resources.A first portion of the pool of physical resources can be included in thestorage allocation. A different, second portion of the pool of physicalresources can be included in the bundle of resources. Yet another, thirdportion of the pool of physical resources can be not allocated beforethe provisioning system is instructed to increase the size of the bundleof resources. In this example, when the size of the bundle of resourcesis increased, physical resources can be taken from the third portion,and allocated to the bundle of resources.

FIG. 9 depicts a simplified diagram of a distributed system 900 forimplementing examples discussed herein. In the illustrated embodiment,distributed system 900 includes one or more client computing devices902, 904, 906, and 908, coupled to a server 912 via one or morecommunication networks 910. Clients computing devices 902, 904, 906, and908 may be configured to execute one or more applications.

In various embodiments, server 912 may be adapted to run one or moreservices or software applications that enable an application executed bya client computing device to specify the application's storage-relatedrequirements and server 912 enables the selection of a storage virtualmachine for servicing the application's storage-related services basedupon the application's specified storage-related requirements, asdescribed in this disclosure. For example, in certain embodiments,server 912 may receive application storage profile information for anapplication, where the application storage profile information includesinformation about the application and also identifies that application'sstorage-related requirements. Server 912 may then generate a uniqueapplication identifier (application ID) for the application and select,based upon the application storage profile information, one or morestorage virtual machines for servicing that application's storage needs.The selected one or more storage virtual machines are ones that cansupport, i.e., can provide or satisfy, the application's storagerequirements specified in the application storage profile informationfor the application. Information identifying the application ID and theselected one or more storage virtual machines is communicated fromserver 912 to a system (application system) that will execute theapplication. For example, if a client computing device is configured toexecute the application, the application ID and selected storage virtualmachine information may be sent by server 912 to that client computingdevice. Information identifying the application ID and the applicationstorage profile information is communicated from server 912 to theselected storage virtual machines. During runtime processing, a storagerequest generated by the application and associated with the applicationID is routed from the device executing the application to a storagevirtual machine that is configured to service that application's storagerequests. The storage virtual machine receiving the storage request isable to determine the corresponding storage-related requirementsassociated with the application ID and service the storage request inaccordance with those storage-related requirements. In this manner,storage services are provided to that application in accordance withthat application's specified storage requirements.

In certain embodiments, server 912 may also provide other services orsoftware applications that can include non-virtual and virtualenvironments. In some embodiments, these services may be offered asweb-based or cloud services, such as under a Software as a Service(SaaS) model to the users of client computing devices 902, 904, 906,and/or 908. Users operating client computing devices 902, 904, 906,and/or 908 may in turn utilize one or more client applications tointeract with server 912 to utilize the services provided by thesecomponents.

In the configuration depicted in FIG. 9, server 912 may include one ormore components 918, 920 and 922 that implement the functions performedby server 912. These components may include software components that maybe executed by one or more processors, hardware components, orcombinations thereof. It should be appreciated that various differentsystem configurations are possible, which may be different fromdistributed system 900. The embodiment shown in FIG. 9 is thus oneexample of a distributed system for implementing an embodiment systemand is not intended to be limiting.

Users may use client computing devices 902, 904, 906, and/or 908 toexecute one or more applications, which may generate one or more storagerequests that may then be serviced in accordance with the teachings ofthis disclosure. A client device may provide an interface that enables auser of the client device to interact with the client device. The clientdevice may also output information to the user via this interface.Although FIG. 9 depicts only four client computing devices, any numberof client computing devices may be supported.

The client devices may include various types of computing systems suchas portable handheld devices, general purpose computers such as personalcomputers and laptops, workstation computers, wearable devices, gamingsystems, thin clients, various messaging devices, sensors or othersensing devices, and the like. These computing devices may run varioustypes and versions of software applications and operating systems (e.g.,Microsoft Windows®, Apple Macintosh®, UNIX® or UNIX-like operatingsystems, Linux or Linux-like operating systems such as Google Chrome™OS) including various mobile operating systems (e.g., Microsoft WindowsMobile®, iOS®, Windows Phone®, Android™, BlackBerry®, Palm OS®).Portable handheld devices may include cellular phones, smartphones,(e.g., an iPhone®), tablets (e.g., iPad®), personal digital assistants(PDAs), and the like. Wearable devices may include Google Glass® headmounted display, and other devices. Gaming systems may include varioushandheld gaming devices, Internet-enabled gaming devices (e.g., aMicrosoft Xbox® gaming console with or without a Kinect® gesture inputdevice, Sony PlayStation® system, various gaming systems provided byNintendo®, and others), and the like. The client devices may be capableof executing various different applications such as variousInternet-related apps, communication applications (e.g., E-mailapplications, short message service (SMS) applications) and may usevarious communication protocols.

Network(s) 910 may be any type of network familiar to those skilled inthe art that can support data communications using any of a variety ofavailable protocols, including without limitation TCP/IP (transmissioncontrol protocol/Internet protocol), SNA (systems network architecture),IPX (Internet packet exchange), AppleTalk®, and the like. Merely by wayof example, network(s) 910 can be a local area network (LAN), networksbased on Ethernet, Token-Ring, a wide-area network (WAN), the Internet,a virtual network, a virtual private network (VPN), an intranet, anextranet, a public switched telephone network (PSTN), an infra-rednetwork, a wireless network (e.g., a network operating under any of theInstitute of Electrical and Electronics (IEEE) 1002.11 suite ofprotocols, Bluetooth®, and/or any other wireless protocol), and/or anycombination of these and/or other networks.

Server 912 may be composed of one or more general purpose computers,specialized server computers (including, by way of example, PC (personalcomputer) servers, UNIX® servers, mid-range servers, mainframecomputers, rack-mounted servers, etc.), server farms, server clusters,or any other appropriate arrangement and/or combination. Server 912 caninclude one or more virtual machines running virtual operating systems,or other computing architectures involving virtualization such as one ormore flexible pools of logical storage devices that can be virtualizedto maintain virtual storage devices for the server. In variousembodiments, server 912 may be adapted to run one or more services orsoftware applications that provide the functionality described in theforegoing disclosure.

The computing systems in server 912 may run one or more operatingsystems including any of those discussed above, as well as anycommercially available server operating system. Server 912 may also runany of a variety of additional server applications and/or mid-tierapplications, including HTTP (hypertext transport protocol) servers, FTP(file transfer protocol) servers, CGI (common gateway interface)servers, JAVA® servers, database servers, and the like. Exemplarydatabase servers include without limitation those commercially availablefrom Oracle®, Microsoft®, Sybase®, IBM® (International BusinessMachines), and the like.

In some implementations, server 912 may include one or more applicationsto analyze and consolidate data feeds and/or event updates received fromusers of client computing devices 902, 904, 906, and 908. As an example,data feeds and/or event updates may include, but are not limited to,Twitter® feeds, Facebook® updates or real-time updates received from oneor more third party information sources and continuous data streams,which may include real-time events related to sensor data applications,financial tickers, network performance measuring tools (e.g., networkmonitoring and traffic management applications), clickstream analysistools, automobile traffic monitoring, and the like. Server 912 may alsoinclude one or more applications to display the data feeds and/orreal-time events via one or more display devices of client computingdevices 902, 904, 906, and 908.

Distributed system 900 may also include one or more data repositories914, 916. These data repositories may be used to store data and otherinformation in certain embodiments. For example, one or more of the datarepositories 914, 916 may be used to store information such asinformation related to storage virtual machines, information mappingapplication IDs to application to selected storage virtual machines, andother information used by server 912 when performing storage controllersystem functions. Data repositories 914, 916 may reside in a variety oflocations. For example, a data repository used by server 912 may belocal to server 912 or may be remote from server 912 and incommunication with server 912 via a network-based or dedicatedconnection. Data repositories 914, 916 may be of different types. Incertain embodiments, a data repository used by server 912 may be adatabase, for example, a relational database, such as databases providedby Oracle Corporation® and other vendors. One or more of these databasesmay be adapted to enable storage, update, and retrieval of data to andfrom the database in response to SQL-formatted commands.

In certain embodiments, one or more of data repositories 914, 916 mayalso be used by applications to store application data. The datarepositories used by applications may be of different types such as, forexample, a key-value store repository, an object store repository, or ageneral storage repository supported by a file system.

In certain embodiments, the storage-related functionalities described inthis disclosure may be offered as services via a cloud environment. FIG.10 is a simplified block diagram of a cloud-based system environment1000 in which various storage-related services may be offered as cloudservices, in accordance with certain embodiments. In the embodimentdepicted in FIG. 10, cloud infrastructure system 1002 may provide one ormore cloud services that may be requested by users using one or moreclient computing devices 1004, 1006, and 1008. Cloud infrastructuresystem 1002 may comprise one or more computers and/or servers that mayinclude those described above for server 912. The computers in cloudinfrastructure system 1002 may be organized as general purposecomputers, specialized server computers, server farms, server clusters,or any other appropriate arrangement and/or combination.

Network(s) 1010 may facilitate communication and exchange of databetween clients 1004, 1006, and 1008 and cloud infrastructure system1002. Network(s) 1010 may include one or more networks. The networks maybe of the same or different types. Network(s) 1010 may support one ormore communication protocols, including wired and/or wireless protocols,for facilitating the communications.

The embodiment depicted in FIG. 10 is only one example of a cloudinfrastructure system and is not intended to be limiting. It should beappreciated that, in some other embodiments, cloud infrastructure system1002 may have more or fewer components than those depicted in FIG. 10,may combine two or more components, or may have a differentconfiguration or arrangement of components. For example, although FIG.10 depicts three client computing devices, any number of clientcomputing devices may be supported in alternative embodiments.

The term cloud service is generally used to refer to a service that ismade available to users on demand and via a communication network suchas the Internet by systems (e.g., cloud infrastructure system 1002) of aservice provider. Typically, in a public cloud environment, servers andsystems that make up the cloud service provider's system are differentfrom the customer's own on-premise servers and systems. The cloudservice provider's systems are managed by the cloud service provider.Customers can thus avail themselves of cloud services provided by acloud service provider without having to purchase separate licenses,support, or hardware and software resources for the services. Forexample, a cloud service provider's system may host an application, anda user may, via the Internet, on demand, order and use the applicationwithout the user having to buy infrastructure resources for executingthe application. Cloud services are designed to provide easy, scalableaccess to applications, resources and services. Several providers offercloud services. For example, several cloud services are offered byOracle Corporation® of Redwood Shores, Calif., such as middlewareservices, database services, Java cloud services, and others.

In certain embodiments, cloud infrastructure system 1002 may provide oneor more cloud services using different models such as under a Softwareas a Service (SaaS) model, a Platform as a Service (PaaS) model, anInfrastructure as a Service (IaaS) model, and others, including hybridservice models. Cloud infrastructure system 1002 may include a suite ofapplications, middleware, databases, and other resources that enableprovision of the various cloud services.

A SaaS model enables an application or software to be delivered to acustomer over a communication network like the Internet, as a service,without the customer having to buy the hardware or software for theunderlying application. For example, a SaaS model may be used to providecustomers access to on-demand applications that are hosted by cloudinfrastructure system 1002. Examples of SaaS services provided by OracleCorporation® include, without limitation, various services for humanresources/capital management, customer relationship management (CRM),enterprise resource planning (ERP), supply chain management (SCM),enterprise performance management (EPM), analytics services, socialapplications, and others.

An IaaS model is generally used to provide infrastructure resources(e.g., servers, storage, hardware and networking resources) to acustomer as a cloud service to provide elastic compute and storagecapabilities. Various IaaS services are provided by Oracle Corporation®.

A PaaS model is generally used to provide, as a service, platform andenvironment resources that enable customers to develop, run, and manageapplications and services without the customer having to procure, build,or maintain such resources. Examples of PaaS services provided by OracleCorporation® include, without limitation, Oracle Java Cloud Service(JCS), Oracle Database Cloud Service (DBCS), data management cloudservice, various application development solutions services, and others.

Cloud services are generally provided on an on-demand self-servicebasis, subscription-based, elastically scalable, reliable, highlyavailable, and secure manner. For example, a customer, via asubscription order, may order one or more services provided by cloudinfrastructure system 1002. Cloud infrastructure system 1002 thenperforms processing to provide the services requested in the customer'ssubscription order. For example, a user may request the cloudinfrastructure system to register an application, as described above,and provide storage-related services to the application per theapplication's specified storage-related requirements. Cloudinfrastructure system 1002 may be configured to provide one or evenmultiple cloud services.

Cloud infrastructure system 1002 may provide the cloud services viadifferent deployment models. In a public cloud model, cloudinfrastructure system 1002 may be owned by a third party cloud servicesprovider and the cloud services are offered to any general publiccustomer, where the customer can be an individual or an enterprise. Incertain other embodiments, under a private cloud model, cloudinfrastructure system 1002 may be operated within an organization (e.g.,within an enterprise organization) and services provided to customersthat are within the organization. For example, the customers may bevarious departments of an enterprise such as the Human Resourcesdepartment, the Payroll department, etc. or even individuals within theenterprise. In certain other embodiments, under a community cloud model,the cloud infrastructure system 1002 and the services provided may beshared by several organizations in a related community. Various othermodels such as hybrids of the above mentioned models may also be used.

Client computing devices 1004, 1006, and 1008 may be of different types(such as devices 902, 904, 906, and 908 depicted in FIG. 9) and may becapable of operating one or more client applications. A user may use aclient device to interact with cloud infrastructure system 1002, such asto request a service provided by cloud infrastructure system 1002. Forexample, a user may use a client device to request a storage-relatedservice described in this disclosure.

In some embodiments, the processing performed by cloud infrastructuresystem 1002 for providing storage-related services may involve big dataanalysis. This analysis may involve using, analyzing, and manipulatinglarge data sets to detect and visualize various trends, behaviors,relationships, etc. within the data. This analysis may be performed byone or more processors, possibly processing the data in parallel,performing simulations using the data, and the like. For example, bigdata analysis may be performed by cloud infrastructure system 1002 fordetermining which storage virtual machine is to be selected for aparticular application based upon the application's statedstorage-related requirements. The data used for this analysis mayinclude structured data (e.g., data stored in a database or structuredaccording to a structured model) and/or unstructured data (e.g., datablobs (binary large objects)).

As depicted in the embodiment in FIG. 10, cloud infrastructure system1002 may include infrastructure resources 1030 that are utilized forfacilitating the provision of various cloud services offered by cloudinfrastructure system 1002. Infrastructure resources 1030 may include,for example, processing resources, storage or memory resources,networking resources, and the like. In certain embodiments, the storagevirtual machines that are available for servicing storage requested fromapplications may be part of cloud infrastructure system 1002. In otherembodiments, the storage virtual machines may be part of differentsystems.

In certain embodiments, to facilitate efficient provisioning of theseresources for supporting the various cloud services provided by cloudinfrastructure system 1002 for different customers, the resources may bebundled into sets of resources or resource modules (also referred to as“pods”). Each resource module or pod may comprise a pre-integrated andoptimized combination of resources of one or more types. In certainembodiments, different pods may be pre-provisioned for different typesof cloud services. For example, a first set of pods may be provisionedfor a database service, a second set of pods, which may include adifferent combination of resources than a pod in the first set of pods,may be provisioned for Java service, and the like. For some services,the resources allocated for provisioning the services may be sharedbetween the services.

Cloud infrastructure system 1002 may itself internally use services 1032that are shared by different components of cloud infrastructure system1002 and which facilitate the provisioning of services by cloudinfrastructure system 1002. These internal shared services may include,without limitation, a security and identity service, an integrationservice, an enterprise repository service, an enterprise managerservice, a virus scanning and white list service, a high availability,backup and recovery service, service for enabling cloud support, anemail service, a notification service, a file transfer service, and thelike.

Cloud infrastructure system 1002 may comprise multiple subsystems. Thesesubsystems may be implemented in software, or hardware, or combinationsthereof. As depicted in FIG. 10, the subsystems may include a userinterface subsystem 1012 that enables users or customers of cloudinfrastructure system 1002 to interact with cloud infrastructure system1002. User interface subsystem 1012 may include various differentinterfaces such as a web interface 1014, an online store interface 1016where cloud services provided by cloud infrastructure system 1002 areadvertised and are purchasable by a consumer, and other interfaces 1018.For example, a customer may, using a client device, request (servicerequest 1034) one or more services provided by cloud infrastructuresystem 1002 using one or more of interfaces 1014, 1016, and 1018. Forexample, a customer may access the online store, browse cloud servicesoffered by cloud infrastructure system 1002, and place a subscriptionorder for one or more services offered by cloud infrastructure system1002 that the customer wishes to subscribe to. The service request mayinclude information identifying the customer and one or more servicesthat the customer desires to subscribe to. For example, a customer mayplace a subscription order for a storage-related service offered bycloud infrastructure system 1002. As part of the order, the customer mayprovide information identifying an application for which the service isto be provided and the application storage profile information for theapplication.

In certain embodiments, such as the embodiment depicted in FIG. 10,cloud infrastructure system 1002 may comprise an order managementsubsystem (OMS) 1020 that is configured to process the new order. Aspart of this processing, OMS 1020 may be configured to: create anaccount for the customer, if not done already; receive billing and/oraccounting information from the customer that is to be used for billingthe customer for providing the requested service to the customer; verifythe customer information; upon verification, book the order for thecustomer; and orchestrate various workflows to prepare the order forprovisioning.

Once properly validated, OMS 1020 may then invoke the order provisioningsubsystem (OPS) 1024 that is configured to provision resources for theorder including processing, memory, and networking resources. Theprovisioning may include allocating resources for the order andconfiguring the resources to facilitate the service requested by thecustomer order. The manner in which resources are provisioned for anorder and the type of the provisioned resources may depend upon the typeof cloud service that has been ordered by the customer. For example,according to one workflow, OPS 1024 may be configured to determine theparticular cloud service being requested and identify a number of podsthat may have been pre-configured for that particular cloud service. Thenumber of pods that are allocated for an order may depend upon thesize/amount/level/scope of the requested service. For example, thenumber of pods to be allocated may be determined based upon the numberof users to be supported by the service, the duration of time for whichthe service is being requested, and the like. The allocated pods maythen be customized for the particular requesting customer for providingthe requested service.

In certain embodiments, setup phase processing, as described above, maybe performed by cloud infrastructure system 1002 as part of theprovisioning process. Cloud infrastructure system 1002 may generate anapplication ID and select a storage virtual machine for an applicationfrom among storage virtual machines provided by cloud infrastructuresystem 1002 itself or from storage virtual machines provided by othersystems other than cloud infrastructure system 1002.

Cloud infrastructure system 1002 may send a response or notification1044 to the requesting customer to indicate when the requested serviceis now ready for use. In some instances, information (e.g., a link) maybe sent to the customer that enables the customer to start using andavailing the benefits of the requested services. In certain embodiments,for a customer requesting the storage-related service, the response mayinclude an application ID generated by cloud infrastructure system 1002and information identifying a storage virtual machine selected by cloudinfrastructure system 1002 for an application corresponding to theapplication ID.

Cloud infrastructure system 1002 may provide services to multiplecustomers. For each customer, cloud infrastructure system 1002 isresponsible for managing information related to one or more subscriptionorders received from the customer, maintaining customer data related tothe orders, and providing the requested services to the customer. Cloudinfrastructure system 1002 may also collect usage statistics regarding acustomer's use of subscribed services. For example, statistics may becollected for the amount of storage used, the amount of datatransferred, the number of users, and the amount of system up time andsystem down time, and the like. This usage information may be used tobill the customer. Billing may be done, for example, on a monthly cycle.

Cloud infrastructure system 1002 may provide services to multiplecustomers in parallel. Cloud infrastructure system 1002 may storeinformation for these customers, including possibly proprietaryinformation. In certain embodiments, cloud infrastructure system 1002comprises an identity management subsystem (IMS) 1028 that is configuredto manage customers information and provide the separation of themanaged information such that information related to one customer is notaccessible by another customer. IMS 1028 may be configured to providevarious security-related services such as identity services, such asinformation access management, authentication and authorizationservices, services for managing customer identities and roles andrelated capabilities, and the like.

FIG. 11 illustrates a computer system 1100 that may be used to implementcertain embodiments. For example, in some embodiments, computer system1100 may be used to implement any of the application system, storagecontroller system, systems within a data center, and various servers andcomputer systems described above. As shown in FIG. 11, computer system1100 includes various subsystems including a processing subsystem 1104that communicates with a number of other subsystems via a bus subsystem1102. These other subsystems may include a processing acceleration unit1106, an I/O subsystem 1108, a storage subsystem 1118, and acommunications subsystem 1124. Storage subsystem 1118 may includenon-transitory computer-readable storage media including storage media1122 and a system memory 1110.

Bus subsystem 1102 provides a mechanism for letting the variouscomponents and subsystems of computer system 1100 communicate with eachother as intended. Although bus subsystem 1102 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 1102 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, a local bus using any of a variety of bus architectures, and thelike. For example, such architectures may include an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard, and the like.

Processing subsystem 1104 controls the operation of computer system 1100and may comprise one or more processors, application specific integratedcircuits (ASICs), or field programmable gate arrays (FPGAs). Theprocessors may include be single core or multicore processors. Theprocessing resources of computer system 1100 can be organized into oneor more processing units 1132, 1134, etc. A processing unit may includeone or more processors, one or more cores from the same or differentprocessors, a combination of cores and processors, or other combinationsof cores and processors. In some embodiments, processing subsystem 1104can include one or more special purpose co-processors such as graphicsprocessors, digital signal processors (DSPs), or the like. In someembodiments, some or all of the processing units of processing subsystem1104 can be implemented using customized circuits, such as applicationspecific integrated circuits (ASICs), or field programmable gate arrays(FPGAs).

In some embodiments, the processing units in processing subsystem 1104can execute instructions stored in system memory 1110 or on computerreadable storage media 1122. In various embodiments, the processingunits can execute a variety of programs or code instructions and canmaintain multiple concurrently executing programs or processes. At anygiven time, some or all of the program code to be executed can beresident in system memory 1110 and/or on computer-readable storage media1122 including potentially on one or more storage devices. Throughsuitable programming, processing subsystem 1104 can provide variousfunctionalities described above. In instances where computer system 1100is executing one or more virtual machines, one or more processing unitsmay be allocated to each virtual machine.

In certain embodiments, a processing acceleration unit 1106 mayoptionally be provided for performing customized processing or foroff-loading some of the processing performed by processing subsystem1104 so as to accelerate the overall processing performed by computersystem 1100.

I/O subsystem 1108 may include devices and mechanisms for inputtinginformation to computer system 1100 and/or for outputting informationfrom or via computer system 1100. In general, use of the term inputdevice is intended to include all possible types of devices andmechanisms for inputting information to computer system 1100. Userinterface input devices may include, for example, a keyboard, pointingdevices such as a mouse or trackball, a touchpad or touch screenincorporated into a display, a scroll wheel, a click wheel, a dial, abutton, a switch, a keypad, audio input devices with voice commandrecognition systems, microphones, and other types of input devices. Userinterface input devices may also include motion sensing and/or gesturerecognition devices such as the Microsoft Kinect® motion sensor thatenables users to control and interact with an input device, theMicrosoft Xbox® 360 game controller, devices that provide an interfacefor receiving input using gestures and spoken commands. User interfaceinput devices may also include eye gesture recognition devices such asthe Google Glass® blink detector that detects eye activity (e.g.,“blinking” while taking pictures and/or making a menu selection) fromusers and transforms the eye gestures as inputs to an input device(e.g., Google Glass®). Additionally, user interface input devices mayinclude voice recognition sensing devices that enable users to interactwith voice recognition systems (e.g., Siri® navigator) through voicecommands.

Other examples of user interface input devices include, withoutlimitation, three dimensional (3-D) mice, joysticks or pointing sticks,gamepads and graphic tablets, and audio/visual devices such as speakers,digital cameras, digital camcorders, portable media players, webcams,image scanners, fingerprint scanners, barcode reader, 3-D scanners, 3-Dprinters, laser rangefinders, and eye gaze tracking devices.Additionally, user interface input devices may include, for example,medical imaging input devices such as computed tomography, magneticresonance imaging, position emission tomography, and medicalultrasonography devices. User interface input devices may also include,for example, audio input devices such as MIDI keyboards, digital musicalinstruments and the like.

In general, use of the term output device is intended to include allpossible types of devices and mechanisms for outputting information fromcomputer system 1100 to a user or other computer. User interface outputdevices may include a display subsystem, indicator lights, or non-visualdisplays such as audio output devices, etc. The display subsystem may bea cathode ray tube (CRT), a flat-panel device, such as that using aliquid crystal display (LCD) or plasma display, a projection device, atouch screen, and the like. For example, user interface output devicesmay include, without limitation, a variety of display devices thatvisually convey text, graphics and audio/video information such asmonitors, printers, speakers, headphones, automotive navigation systems,plotters, voice output devices, and modems.

Storage subsystem 1118 provides a repository or data store for storinginformation and data that is used by computer system 1100. Storagesubsystem 1118 provides a tangible non-transitory computer-readablestorage medium for storing the basic programming and data constructsthat provide the functionality of some embodiments. Storage subsystem1118 may store software (e.g., programs, code modules, instructions)that when executed by processing subsystem 1104 provides thefunctionality described above. The software may be executed by one ormore processing units of processing subsystem 1104. Storage subsystem1118 may also provide a repository for storing data used in accordancewith the teachings of this disclosure.

Storage subsystem 1118 may include one or more non-transitory memorydevices, including volatile and non-volatile memory devices. As shown inFIG. 11, storage subsystem 1118 includes a system memory 1110 and acomputer-readable storage media 1122. System memory 1110 may include anumber of memories including a volatile main random access memory (RAM)for storage of instructions and data during program execution and anon-volatile read only memory (ROM) or flash memory in which fixedinstructions are stored. In some implementations, a basic input/outputsystem (BIOS), containing the basic routines that help to transferinformation between elements within computer system 1100, such as duringstart-up, may typically be stored in the ROM. The RAM typically containsdata and/or program modules that are presently being operated andexecuted by processing subsystem 1104. In some implementations, systemmemory 1110 may include multiple different types of memory, such asstatic random access memory (SRAM), dynamic random access memory (DRAM),and the like.

By way of example, and not limitation, as depicted in FIG. 11, systemmemory 1110 may load application programs 1112 that are being executed,which may include various applications such as Web browsers, mid-tierapplications, relational database management systems (RDBMS), etc.,program data 1111, and an operating system 1116. By way of example,operating system 1116 may include various versions of MicrosoftWindows®, Apple Macintosh®, and/or Linux operating systems, a variety ofcommercially-available UNIX® or UNIX-like operating systems (includingwithout limitation the variety of GNU/Linux operating systems, theGoogle Chrome® OS, and the like) and/or mobile operating systems such asiOS, Windows® Phone, Android® OS, BlackBerry® OS, Palm® OS operatingsystems, and others.

Computer-readable storage media 1122 may store programming and dataconstructs that provide the functionality of some embodiments.Computer-readable storage media 1122 may provide storage ofcomputer-readable instructions, data structures, program modules, andother data for computer system 1100. Software (programs, code modules,instructions) that, when executed by processing subsystem 1104 providesthe functionality described above, may be stored in storage subsystem1118. By way of example, computer-readable storage media 1122 mayinclude non-volatile memory such as a hard disk drive, a magnetic diskdrive, an optical disk drive such as a CD ROM, DVD, a Blu-Ray® disk, orother optical media. Computer-readable storage media 1122 may include,but is not limited to, Zip® drives, flash memory cards, universal serialbus (USB) flash drives, secure digital (SD) cards, DVD disks, digitalvideo tape, and the like. Computer-readable storage media 1122 may alsoinclude, solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.

In certain embodiments, storage subsystem 1118 may also include acomputer-readable storage media reader 1120 that can further beconnected to computer-readable storage media 1122. Reader 1120 mayreceive and be configured to read data from a memory device such as adisk, a flash drive, etc.

In certain embodiments, computer system 1100 may support virtualizationtechnologies, including but not limited to virtualization of processingand memory resources. For example, computer system 1100 may providesupport for executing one or more virtual machines. In certainembodiments, computer system 1100 may execute a program such as ahypervisor that facilitated the configuring and managing of the virtualmachines. Each virtual machine may be allocated memory, compute (e.g.,processors, cores), I/O, and networking resources. Each virtual machinegenerally runs independently of the other virtual machines. A virtualmachine typically runs its own operating system, which may be the sameas or different from the operating systems executed by other virtualmachines executed by computer system 1100. Accordingly, multipleoperating systems may potentially be run concurrently by computer system1100.

Communications subsystem 1124 provides an interface to other computersystems and networks. Communications subsystem 1124 serves as aninterface for receiving data from and transmitting data to other systemsfrom computer system 1100. For example, communications subsystem 1124may enable computer system 1100 to establish a communication channel toone or more client devices via the Internet for receiving and sendinginformation from and to the client devices.

Communication subsystem 1124 may support both wired and/or wirelesscommunication protocols. For example, in certain embodiments,communications subsystem 1124 may include radio frequency (RF)transceiver components for accessing wireless voice and/or data networks(e.g., using cellular telephone technology, advanced data networktechnology, such as 3G, 4G or EDGE (enhanced data rates for globalevolution), WiFi (IEEE 802.XX family standards, or other mobilecommunication technologies, or any combination thereof), globalpositioning system (GPS) receiver components, and/or other components.In some embodiments communications subsystem 1124 can provide wirednetwork connectivity (e.g., Ethernet) in addition to or instead of awireless interface.

Communication subsystem 1124 can receive and transmit data in variousforms. For example, in some embodiments, in addition to other forms,communications subsystem 1124 may receive input communications in theform of structured and/or unstructured data feeds 1126, event streams1128, event updates 1130, and the like. For example, communicationssubsystem 1124 may be configured to receive (or send) data feeds 1126 inreal-time from users of social media networks and/or other communicationservices such as Twitter® feeds, Facebook® updates, web feeds such asRich Site Summary (RSS) feeds, and/or real-time updates from one or morethird party information sources.

In certain embodiments, communications subsystem 1124 may be configuredto receive data in the form of continuous data streams, which mayinclude event streams 1128 of real-time events and/or event updates1130, that may be continuous or unbounded in nature with no explicitend. Examples of applications that generate continuous data may include,for example, sensor data applications, financial tickers, networkperformance measuring tools (e.g. network monitoring and trafficmanagement applications), clickstream analysis tools, automobile trafficmonitoring, and the like.

Communications subsystem 1124 may also be configured to communicate datafrom computer system 1100 to other computer systems or networks. Thedata may be communicated in various different forms such as structuredand/or unstructured data feeds 1126, event streams 1128, event updates1130, and the like to one or more databases that may be in communicationwith one or more streaming data source computers coupled to computersystem 1100.

Computer system 1100 can be one of various types, including a handheldportable device (e.g., an iPhone® cellular phone, an iPad® computingtablet, a PDA), a wearable device (e.g., a Google Glass® head mounteddisplay), a personal computer, a workstation, a mainframe, a kiosk, aserver rack, or any other data processing system. Due to theever-changing nature of computers and networks, the description ofcomputer system 1100 depicted in FIG. 11 is intended only as a specificexample. Many other configurations having more or fewer components thanthe system depicted in FIG. 11 are possible. Based on the disclosure andteachings provided herein, a person of ordinary skill in the art willappreciate other ways and/or methods to implement the variousembodiments.

Although specific embodiments have been described, variousmodifications, alterations, alternative constructions, and equivalentsare possible. Embodiments are not restricted to operation within certainspecific data processing environments, but are free to operate within aplurality of data processing environments. Additionally, althoughcertain embodiments have been described using a particular series oftransactions and steps, it should be apparent to those skilled in theart that this is not intended to be limiting. Although some flowchartsdescribe operations as a sequential process, many of the operations canbe performed in parallel or concurrently. In addition, the order of theoperations may be rearranged. A process may have additional steps notincluded in the figure. Various features and aspects of theabove-described embodiments may be used individually or jointly.

Further, while certain embodiments have been described using aparticular combination of hardware and software, it should be recognizedthat other combinations of hardware and software are also possible.Certain embodiments may be implemented only in hardware, or only insoftware, or using combinations thereof. The various processes describedherein can be implemented on the same processor or different processorsin any combination.

Where devices, systems, components or modules are described as beingconfigured to perform certain operations or functions, suchconfiguration can be accomplished, for example, by designing electroniccircuits to perform the operation, by programming programmableelectronic circuits (such as microprocessors) to perform the operationsuch as by executing computer instructions or code, or processors orcores programmed to execute code or instructions stored on anon-transitory memory medium, or any combination thereof. Processes cancommunicate using a variety of techniques including but not limited toconventional techniques for inter-process communications, and differentpairs of processes may use different techniques, or the same pair ofprocesses may use different techniques at different times.

Specific details are given in this disclosure to provide a thoroughunderstanding of the embodiments. However, embodiments may be practicedwithout these specific details. For example, well-known circuits,processes, algorithms, structures, and techniques have been shownwithout unnecessary detail in order to avoid obscuring the embodiments.This description provides example embodiments only, and is not intendedto limit the scope, applicability, or configuration of otherembodiments. Rather, the preceding description of the embodiments willprovide those skilled in the art with an enabling description forimplementing various embodiments. Various changes may be made in thefunction and arrangement of elements.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that additions, subtractions, deletions, and other modificationsand changes may be made thereunto without departing from the broaderspirit and scope as set forth in the claims. Thus, although specificembodiments have been described, these are not intended to be limiting.Various modifications and equivalents are within the scope of thefollowing claims.

What is claimed is:
 1. A computer-implemented method, comprising:monitoring, by a computing system operating in a data center, changes todata in a storage allocation, wherein the storage allocation includes aset of computing resources from computing resources of the data center,wherein the storage allocation is associated with a tenant of the datacenter, wherein the data center enables users associated with the tenantto use the set of computing resources during a subscription period, andwherein the storage allocation is associated with a category from aplurality of categories for storage allocations; determining an expectedresource usage for the storage allocation, wherein the expected resourceusage projects an amount of computing resources the storage allocationwill use after a period of time following a current time, and whereinthe expected resource usage is determined using the changes to the data;determining that the expected resource usage is greater than a size of abundle of resources from a resource pool, wherein the bundle ofresources includes a set of unused computing resources that has beenpre-allocated for use as a new storage allocation of a same category asthe category for the storage allocation, wherein the size of the bundleof resources corresponds to an amount of the set of unused computingresources; and instructing a provisioning system of the data center toincrease the size of the bundle of resources to correspond to theexpected resource usage for the storage allocation, wherein increasingthe size of the bundle of resources includes allocating additionalunused computing resources to the bundle of resources.
 2. Thecomputer-implemented method of claim 1, wherein, before the provisioningsystem is configured to increase the size of the bundle of resources,the bundle of resources has a first size, wherein the storage allocationwas configured from a second bundle of resources from the resource pool,the second bundle of resources being the first size.
 3. Thecomputer-implemented method of claim 1, further comprising: receiving arequest for a second storage allocation; determining that the request isassociated with a subscription of the same category as the category forthe storage allocation; and instructing the provisioning system toconfigure the bundle of resources according to the request, wherein,when configured, the set of unused computing resources and theadditional unused computing resources included in the bundle ofresources are assigned to the second storage allocation.
 4. Thecomputer-implemented method of claim 1, further comprising: determiningan expected number of storage allocations for the category, wherein theexpected number of storage allocations project storage allocations forsubscriptions expected to be received after the current time;determining that the expected number of storage allocations is greaterthan a number of bundles of resources associated with the category; andallocating additional bundles of resources, wherein allocating theadditional bundles of resources includes allocating additional unusedcomputing resources to each of the additional bundles of resources. 5.The computer-implemented method of claim 1, wherein unused computingresources are computing resources that are not allocated to storageallocations.
 6. The computer-implemented method of claim 1, whereinincreasing the size of the bundle of resources includes allocatingunused physical storage to the bundle of resources.
 7. Thecomputer-implemented method of claim 1, wherein the data center includesa pool of physical resources, wherein a first portion of the pool ofphysical resources is included in the storage allocation, wherein asecond portion of the pool of physical resources is included in thebundle of resources, wherein a third portion of the pool of physicalresources is not allocated before the provisioning system is instructedto increase the size of the bundle of resources.
 8. Thecomputer-implemented method of claim 1, wherein computing resourcesinclude processing resources, storage resources, or networkingresources.
 9. The computer-implemented method of claim 1, furthercomprising: determining a mapping between a functional entity of thestorage allocation and physical storage associated with the storageallocation, wherein a functional entity represents data in the storageallocation, and wherein the expected resource usage is determined usingthe mapping.
 10. A computing system operating in a data center,comprising: one or more processors; and a non-transitorycomputer-readable medium communicatively coupled to the one or moreprocessors, the non-transitory computer-readable medium includinginstructions that, when executed by the one or more processors, causethe one or more processors to perform operations including: monitoringchanges to data in a storage allocation, wherein the storage allocationincludes a set of computing resources from computing resources of thedata center, wherein the storage allocation is associated with a tenantof the data center, wherein the data center enables users associatedwith the tenant to use the set of computing resources during asubscription period, and wherein the storage allocation is associatedwith a category from a plurality of categories for storage allocations;determining an expected resource usage for the storage allocation,wherein the expected resource usage projects an amount of computingresources the storage allocation will use after a period of timefollowing a current time, and wherein the expected resource usage isdetermined using the changes to the data; determining that the expectedresource usage is greater than a size of a bundle of resources from aresource pool, wherein the bundle of resources includes a set of unusedcomputing resources that has been pre-allocated for use as a new storageallocation of a same category as the category for the storageallocation, wherein the size of the bundle of resources corresponds toan amount of the set of unused computing resources; and instructing aprovisioning system of the data center to increase the size of thebundle of resources to correspond to the expected resource usage for thestorage allocation, wherein increasing the size of the bundle ofresources includes allocating additional unused computing resources tothe bundle of resources.
 11. The computing system of claim 10, wherein,before the provisioning system is configured to increase the size of thebundle of resources, the bundle of resources has a first size, whereinthe storage allocation was configured from a second bundle of resourcesfrom the resource pool, the second bundle of resources being the firstsize.
 12. The computing system of claim 10, wherein the non-transitorycomputer-readable medium further includes instructions that, whenexecuted by the one or more processors, cause the one or more processorsto perform operations including: receiving a request for a secondstorage allocation; determining that the request is associated with asubscription of the same category as the category for the storageallocation; and instructing the provisioning system to configure thebundle of resources according to the request, wherein, when configured,the set of unused computing resources and the additional unusedcomputing resources included in the bundle of resources are assigned tothe second storage allocation.
 13. The computing system of claim 10,wherein the non-transitory computer-readable medium further includesinstructions that, when executed by the one or more processors, causethe one or more processors to perform operations including: determiningan expected number of storage allocations for the category, wherein theexpected number of storage allocations project storage allocations forsubscriptions expected to be received after the current time;determining that the expected number of storage allocations is greaterthan a number of bundles of resources associated with the category; andallocating additional bundles of resources, wherein allocating theadditional bundles of resources includes allocating additional unusedcomputing resources to each of the additional bundles of resources. 14.The computing system of claim 10, wherein unused computing resources arecomputing resources that are not allocated to storage allocations. 15.The computing system of claim 10, wherein increasing the size of thebundle of resources includes allocating unused physical storage to thebundle of resources.
 16. A non-transitory computer-readable mediumincluding instructions that, when executed by one or more processors,cause the one or more processors to perform operations including:monitoring changes to data in a storage allocation in a data center,wherein the storage allocation includes a set of computing resourcesfrom computing resources of the data center, wherein the storageallocation is associated with a tenant of the data center, wherein thedata center enables users associated with the tenant to use the set ofcomputing resources during a subscription period, and wherein thestorage allocation is associated with a category from a plurality ofcategories for storage allocations; determining an expected resourceusage for the storage allocation, wherein the expected resource usageprojects an amount of computing resources the storage allocation willuse after a period of time following a current time, and wherein theexpected resource usage is determined using the changes to the data;determining that the expected resource usage is greater than a size of abundle of resources from a resource pool, wherein the bundle ofresources includes a set of unused computing resources that has beenpre-allocated for use as a new storage allocation of a same category asthe category for the storage allocation, wherein the size of the bundleof resources corresponds to an amount of the set of unused computingresources; and instructing a provisioning system of the data center toincrease the size of the bundle of resources to correspond to theexpected resource usage for the storage allocation, wherein increasingthe size of the bundle of resources includes allocating additionalunused computing resources to the bundle of resources.
 17. Thenon-transitory computer-readable medium of claim 16, wherein, before theprovisioning system is configured to increase the size of the bundle ofresources, the bundle of resources has a first size, wherein the storageallocation was configured from a second bundle of resources from theresource pool, the second bundle of resources being the first size. 18.The non-transitory computer-readable medium of claim 16, wherein thenon-transitory computer-readable medium further includes instructionsthat, when executed by the one or more processors, cause the one or moreprocessors to perform operations including: receiving a request for asecond storage allocation; determining that the request is associatedwith a subscription of the same category as the category for the storageallocation; and instructing the provisioning system to configure thebundle of resources according to the request, wherein, when configured,the set of unused computing resources and the additional unusedcomputing resources included in the bundle of resources are assigned tothe second storage allocation.
 19. The non-transitory computer-readablemedium of claim 16, wherein the non-transitory computer-readable mediumfurther includes instructions that, when executed by the one or moreprocessors, cause the one or more processors to perform operationsincluding: determining an expected number of storage allocations for thecategory, wherein the expected number of storage allocations projectstorage allocations for subscriptions expected to be received after thecurrent time; determining that the expected number of storageallocations is greater than a number of bundles of resources associatedwith the category; and allocating additional bundles of resources,wherein allocating the additional bundles of resources includesallocating additional unused computing resources to each of theadditional bundles of resources.
 20. The non-transitorycomputer-readable medium of claim 16, wherein unused computing resourcesare computing resources that are not allocated to storage allocations.21. The non-transitory computer-readable medium of claim 16, whereinincreasing the size of the bundle of resources includes allocatingunused physical storage to the bundle of resources.